# Bayesian spatial monotonic multiple regression

Bayesian spatial monotonic multiple regression Summary We consider monotonic, multiple regression for contiguous regions. The regression functions vary regionally and may exhibit spatial structure. We develop Bayesian nonparametric methodology that permits estimation of both continuous and discontinuous functional shapes using marked point process and reversible jump Markov chain Monte Carlo techniques. Spatial dependence is incorporated by a flexible prior distribution which is tuned using crossvalidation and Bayesian optimization. We derive the mean and variance of the prior induced by the marked point process approach. Asymptotic results show consistency of the estimated functions. Posterior realizations enable variable selection, the detection of discontinuities and prediction. In simulations and in an application to a Norwegian insurance dataset, our method shows better performance than existing approaches. 1. Introduction Geospatial data arise in forestry (Penttinen et al., 1992), epidemiology (Waller & Gotway, 2004) and other domains. Due to practicality or confidentiality concerns, locally aggregated data are common and are typically available on an irregular lattice. Statistical methods for such data aim to explore the association between a response and explanatory variables while accounting for spatial dependence in the model parameters. To introduce such dependence, a neighbourhood structure, often based upon the arrangement of the areal units on a map, is typically defined via an adjacency matrix. Most modelling frameworks assume a common effect of the explanatory variables for all regions (Waller & Gotway, 2004; Wakefield, 2007). Spatial variation is then typically accommodated via a spatially structured random effect on the intercept. Some applications, however, need to allow for a spatially varying regression function (Bell et al., 2004; Zhang & Shi, 2004; Cahill & Mulligan, 2007). Statistical methods for such scenarios are available for generalized linear (Fotheringham et al., 2003; Assunção, 2003; Scheel et al., 2013) and additive models (Congdon, 2006). However, these approaches are limited, as continuity of the regression function is assumed: abrupt changes in the regression surface are not captured unless they are explicitly included in the model. Neglecting such effects may result in a bias due to oversmoothing; see Bowman & Azzalini (1997, p. 26). Since continuity may be inappropriate, we replace it by monotonicity (Royston, 2000; Farah et al., 2013; Wilson et al., 2014). Whilst continuity cannot, in general, be verified, tests of monotonicity are available (Bowman et al., 1998; Ghosal et al., 2000; Scott et al., 2015). Based upon a number of observations for each region, we develop Bayesian nonparametric methodology which estimates the regional regression functions whilst exploiting any neighbourhood structure. The estimation of a single monotonic function is usually called isotonic regression. Early publications discussed inference under monotonicity constraints (Ayer et al., 1955; Brunk, 1955; Barlow & Brunk, 1972), and solution algorithms are available (Brunk et al., 1957; Luss et al., 2012). Isotonic regression is further considered for additive (Bacchetti, 1989; Tutz & Leitenstorfer, 2007) and high-dimensional models (Fang & Meinshausen, 2012; Bergersen et al., 2014), in functional data analysis (Ramsay, 1998; Ramsay & Silverman, 2005) and in Bayesian nonparametric modelling (Gelfand & Kuo, 1991; Shively et al., 2009; Saarela & Arjas, 2011; Lin & Dunson, 2014). In order to learn about potentially spatially structured monotonic regression functions, a dependence model for functions, possibly with discontinuities, is required. Our approach represents each monotonic regional function by a set of marked point processes. Potential spatial structure is modelled via a joint prior distribution, which is based upon a flexible discrepancy measure. The prior allows the functional dependence to be constant, increasing or decreasing with increasing function values. The Bayesian framework induces a consistent posterior (Barron et al., 1999; Walker & Hjort, 2001), and permits both smooth contours and discontinuities. To tune the prior, we combine crossvalidation and Bayesian optimization. Realizations of the posterior are obtained by a reversible jump Markov chain Monte Carlo algorithm (Green 1995) and enable variable selection, prediction and the detection of discontinuities. 2. Modelling and inference 2.1. Likelihood and notation Consider $$K$$ contiguous regions whose neighbourhood structure is given by an adjacency matrix or a lattice graph. Let $$y_k\in\mathbb{R}$$ and $$x_k\in\mathbb{R}^m$$ denote the response and explanatory variables, respectively, for region $$k$$ ($$k=1,\ldots,K$$). The likelihood is $$f\left\{\,y_k\mid\lambda_k(x_k), \theta_k\right\}\!,$$ (1) where $$\lambda_k:\mathbb{R}^m\to\left[\delta_{\min}, \delta_{\max}\right]$$ is the monotonic regression function for region $$k$$, for which $$\lambda_k\left(x_k\right)$$ is assumed to lie within the predefined interval $$\left[\delta_{\min}, \delta_{\max}\right]$$. Monotonicity is defined in terms of the partial Euclidean ordering $$\preceq$$: if $$u\leqslant v$$ componentwise, then $$\lambda_k(u)\leqslant \lambda_k(v)$$, for $$u,v\in \mathbb{R}^m$$. The vector $$\theta_k$$ denotes additional, potentially spatially varying, model parameters which are a priori independent of $$\lambda_1,\ldots,\lambda_K$$. In what follows, we perform inference on $$\lambda_1$$ through $$\lambda_K$$ while accounting for potential spatial structure in these functions. Each $$\lambda_k$$ ($$k=1,\ldots,K$$) is estimated over a closed set $$X\subset\mathbb{R}^m$$. In applications, $$X$$ and $$[\delta_{\min},\delta_{\max}]$$ may be defined in terms of the explanatory variables and responses, respectively. For instance, if $$\lambda_k\left(x_k\right)$$ in (1) is the mean response, $$\delta_{\min}$$ may be set to the minimum observed response across the $$K$$ regions. In § 2.2–2.4 we complete the Bayesian framework by defining a joint prior on $$\lambda_1,\ldots,\lambda_K$$, while § 2.5 and 2.6 detail the estimation procedure. 2.2. A spatial dependence model for the monotonic functions We wish to impose spatial structure on $$\lambda_1,\ldots,\lambda_K$$ and hence define a joint prior density that favours these functions being similar. We set $$\delta_{\min}=0$$ and write $$\lambda_k(x)$$ instead of $$\lambda_k(x)-\delta_{\min}$$ ($$k=1,\ldots,K$$) below. First, we introduce a pairwise discrepancy measure for $$\lambda_k$$ and $$\lambda_{k'}$$ ($$k,k'=1,\ldots,K; k\neq k'$$). Such a measure should be minimal if and only if $$\lambda_k$$ and $$\lambda_{k'}$$ are equal, and should increase with an increasing difference in these functions. A possible choice is the integrated squared distance $$\int_{X} \left\{\lambda_k(x) - \lambda_{k'}(x)\right\}^2\,\mbox{d}x\text{.}$$ (2) Sometimes we have prior knowledge that differences in the lower, or higher, function values of $$\lambda_k$$ and $$\lambda_{k'}$$ should be downweighted, or avoided. For example, increased measurement error in higher values of the explanatory variables may be better handled through increased information borrowing. Thus, we replace $$\left\{\lambda_k(x) - \lambda_{k'}(x)\right\}^2$$ in (2) by $$\gamma_{p}\left\{\lambda_k(x),\lambda_{k'}(x)\right\}=\left[\left\{\lambda_k(x)\right\}^p-\left\{\lambda_{k'}(x)\right\}^p\right]\left\{\lambda_k(x)-\lambda_{k'}(x)\right\}\!,\qquad p>0,$$ (3) for which $$p=1$$ yields the integrated squared distance. See the 2017 Lancaster University PhD thesis by C. Rohrbeck for a more general formulation. Expression (3) can also be interpreted as the squared distance with weight $$\left[\left\{\lambda_k(x)\right\}^p-\left\{\lambda_{k'}(x)\right\}^p\right]/ \left\{\lambda_k(x)-\lambda_{k'}(x)\right\}$$. Figure 1 illustrates the behaviour of $$\gamma_{p}\left\{\lambda_k(x),\lambda_{k'}(x)\right\}$$ at a fixed point $$x\in\mathbb{R}^m$$ for different settings of $$p$$. For brevity, let $$\kappa=\lambda_k(x)$$ and $$\psi=\lambda_{k'}(x)$$. Figure 1(a) shows that $$\gamma_p\{\kappa,\psi\}$$ increases with an increasing difference between $$\kappa$$ and $$\psi=0$$ for all settings of $$p$$. Hence, $$\gamma_p$$ satisfies the desired properties stated above. Furthermore in Fig. 1(b), the fixed difference $$\psi = \kappa + 1$$ is penalized more for higher $$\kappa$$ if $$p>1$$, while being penalized less for $$p<1$$. A constant penalty is induced for $$p=1$$. As such, the parameter $$p$$ allows the penalty for differences between $$\lambda_k$$ and $$\lambda_{k'}$$ to vary with the function values. Fig. 1. View largeDownload slide Behaviour of $$\gamma_{p}\left\{\kappa,\psi\right\}$$ with respect to $$\kappa$$ for $$p=1$$ (solid), $$p=2$$ (dashes), $$p=0{\cdot}5$$ (dots) and $$p=0{\cdot}2$$ (dot-dash), subject to (a) $$\psi =0$$ and (b) $$\psi = \kappa + 1$$ being fixed. Fig. 1. View largeDownload slide Behaviour of $$\gamma_{p}\left\{\kappa,\psi\right\}$$ with respect to $$\kappa$$ for $$p=1$$ (solid), $$p=2$$ (dashes), $$p=0{\cdot}5$$ (dots) and $$p=0{\cdot}2$$ (dot-dash), subject to (a) $$\psi =0$$ and (b) $$\psi = \kappa + 1$$ being fixed. The dependence model for the $$K$$-set $$\lambda_1,\ldots,\lambda_K$$ is then defined as a Gibbs measure (Georgii, 2011) with the discrepancy measure constructed in (2) and (3) as a pair-potential. Formally, $$\pi\left(\lambda_1, \ldots, \lambda_K\mid\omega\right) \propto \prod_{1\leqslant k<k'\leqslant K} \exp \left[-\omega\ d_{k,k'}\int_{X}\gamma_{p}\left\{\lambda_k(x),\lambda_{k'}(x)\right\}\mbox{d}x\right]\!,\qquad\omega\geqslant0,$$ (4) where the product is over all pairs of regions. The constant $$d_{k,k'}\geqslant0$$ describes our prior belief concerning the degree of similarity of $$\lambda_k$$ and $$\lambda_{k'}$$. In spatial statistics, we often set $$d_{k,k'}=1$$ if the regions $$k$$ and $$k'$$ are adjacent and $$d_{k,k'}=0$$ otherwise. Such a choice reduces the computational cost since the integral in (4) need only be evaluated for pairs of adjacent regions. The degree of dependence increases in $$\omega$$, and $$\omega=0$$ corresponds to $$\lambda_1,\ldots,\lambda_K$$ being independent. Sensitivity to choice of $$p$$ is explored in § 3.3. Expression (4) can be extended to regionally varying $$X$$, permitting borrowing of information for extrapolation; see the Supplementary Material. 2.3. Marked point process prior We specify an individual prior model for $$\lambda_k:X\to\left[\delta_{\min},\delta_{\max}\right]$$ ($$k=1,\ldots,K$$) and drop the index $$k$$ in the rest of this subsection for brevity. Prior distributions proposed in the literature include an ordered Dirichlet process (Gelfand & Kuo, 1991) and a constrained spline (Shively et al., 2009). Our prior is similar to that of Saarela & Arjas (2011): $$\lambda$$ is postulated to be a nondecreasing step function with $$\lambda(x)\in\left[\delta_{\min},\delta_{\max}\right]$$; any monotonic, bounded function can be approximated to a desired accuracy by increasing the number of steps. The location and height of the steps of $$\lambda$$ define a marked point process on $$X$$. Following Saarela & Arjas (2011), we represent $$\lambda$$ via a set of $$I$$ marked point processes, $$\Delta = \left(\Delta_1,\ldots,\Delta_I\right)$$, where $$\Delta_i$$ ($$i=1,\ldots,I$$) is on a set $$X_i$$ with $$\bigcup_{i=1}^I X_i=X$$. Here, we define $$X_1,\ldots,X_I$$ based on the nonempty subsets of $$\left\{1,\ldots,m\right\}$$. For example, if $$m=2$$ we choose $$I=3$$ and have separate processes $$\Delta_1$$ and $$\Delta_2$$ for each of the two explanatory variables, $$x_1$$ and $$x_2$$, respectively, and one process $$\Delta_3$$ for both components, $$\left(x_1,x_2\right)$$, jointly. Figure 2 provides an example for $$X=[0,1]^2$$. The benefits of this representation are discussed later in this subsection. Fig. 2. View largeDownload slide Point locations to represent a step function $$\lambda$$ on $$X=[0,1]^2$$ via a set of $$I=3$$ marked point processes $$(\Delta_1,\Delta_2,\Delta_3)$$. The processes $$\Delta_1$$ (triangle), $$\Delta_2$$ (square) and $$\Delta_3$$ (diamond) are defined on the sets $$X_1=[0,1]\times 0$$, $$X_2=0\times[0,1]$$ and $$X_3=(0,1]\times(0,1]$$, respectively. Fig. 2. View largeDownload slide Point locations to represent a step function $$\lambda$$ on $$X=[0,1]^2$$ via a set of $$I=3$$ marked point processes $$(\Delta_1,\Delta_2,\Delta_3)$$. The processes $$\Delta_1$$ (triangle), $$\Delta_2$$ (square) and $$\Delta_3$$ (diamond) are defined on the sets $$X_1=[0,1]\times 0$$, $$X_2=0\times[0,1]$$ and $$X_3=(0,1]\times(0,1]$$, respectively. We now formalize the representation of $$\lambda$$ via $$\Delta$$ and let \begin{equation*} \Delta_{i} = \left\{\left(\xi_{i,j}, \delta_{i,j}\right)\in X_i\times\left[\delta_{\min},\delta_{\max}\right] {\ :\ \ } j = 1,\ldots,n_{i}\right\}\qquad (i=1,\ldots,I)\text{.} \end{equation*} Here, $$\xi_{i,j}$$ and $$\delta_{i,j}$$ refer to a point location and associated mark, respectively, and $$n_i$$ is the number of points in $$\Delta_i$$. Monotonicity is imposed by constraining the marks: if $$\xi_{i,j} \preceq \xi_{i',j'}$$, then $$\delta_{i,j} \leqslant \delta_{i',j'}$$ ($$i,i'=1,\ldots,I$$; $$j=1,\ldots,n_i$$; $$j'=1,\ldots,n_{i'})$$. The value $$\lambda(x)$$ is then defined as the largest mark $$\delta_{i,j}$$ such that $$x$$ imposes a monotonicity constraint on the associated point location $$\xi_{i,j}$$. Formally, $$\lambda(x) = \max_{i,j} \left\{\delta_{i,j} {\ :\ } \xi_{i,j}\preceq x\right\}\!\text{.}$$ (5) Representing $$\lambda$$ via the set $$(\Delta_1,\ldots,\Delta_I)$$ facilitates variable selection. Let $$X=[0,1]^m$$ and suppose that the explanatory variable $$x_1$$ is redundant. Hence, $$\lambda$$ is constant with increasing values of $$x_1$$, that is, $$\lambda(x) = \lambda\left\{x + (\epsilon, 0,\ldots,0)\right\}$$ ($$x\in X; \epsilon>0$$). As we represent $$\lambda$$ via a marked point process, the redundancy of $$x_1$$ implies that the point locations are in the set $$0 \times [0,1]^{m-1}$$. For instance, if $$m=2$$, all points then lie on the line $$x_1=0$$ in Fig. 2. As such, the processes $$\Delta_1$$ and $$\Delta_3$$ contain no points. Consequently, $$n_i$$ ($$i=1,\ldots,I$$) provides an indicator of the redundancy of explanatory variables. The association defined in (5) results in a mapping between the spaces of step functions and marked point processes with monotonicity constraints. We define a prior for $$\lambda$$ via one for $$\Delta$$. The prior on $$N\,{=}\,\sum_{i=1}^{I} n_i$$, the number of steps representing $$\lambda$$, is chosen to be geometric; with $$P(N\,{=}\,n)\,{=}\,(1-\eta)\eta^n$$ for $$n\,{=}\,0,1,\ldots$$, for some specified hyperparameter $$\eta\,{>}\,1$$. This choice allows the possibility that $$N\,{=}\,0$$, which corresponds to $$\lambda\,{=}\,\delta_{min}$$ being constant. This choice promotes model parsimony and favours $$\lambda$$ having few steps. Given $$N$$, the vector $$\left(n_1,\ldots,n_I\right)$$ is uniformly distributed over the set of possibilities of allocating $$N$$ points to the $$I$$ processes. For $$\Delta_i$$ ($$i=1,\ldots,I$$), the location $$\xi_{i,j}$$ ($$\,j\,{=}\,1,\ldots,n_i$$) is uniformly distributed on $$X_i$$. The marks $$\left\{\delta_{i,j}: j=1,\ldots,n_i; i=1,\ldots,I\right\}$$ are uniformly distributed on $$\left[\delta_{\min},\delta_{\max}\right]$$, subject to the monotonicity constraints imposed by the locations in $$\Delta_1,\ldots,\Delta_I$$. Using this hierarchical structure, we obtain the prior density $$\phi\left(\Delta\mid \eta\right) = \pi\left(\left\{\delta_{i,j}\right\} \mid\left\{\xi_{i,j}\right\}\right) \left\{\prod_{i=1}^I\prod_{j=1}^{n_i} \pi\left(\xi_{i,j}\right)\right\} \pi\left(n_1,\ldots,n_I\mid N\right)\pi\left(N\mid\eta\right)\!;$$ (6) further details are provided in the Supplementary Material. The density $$\phi\left(\Delta\mid \eta\right)$$ induces a density on the space of step functions, $$\tilde{\phi}\left(\lambda\mid\eta\right)$$, which can be characterized as follows. Proposition 1. Let $$X=[0,1], \delta_{\min}=0$$ and $$\delta_{\max}=1$$. Then the distribution with density $$\tilde{\phi}\left(\lambda\mid\eta\right)$$ has \begin{align*} E\left\{\lambda(x)\mid\eta\right\} &= x \sum_{n=1}^\infty \left\{\frac{1}{\eta}\left(1-\frac{1}{\eta}\right)^n \frac{n}{n+1}\right\} = x\left(1-\frac{\log\eta}{\eta-1}\right)\!,\\ {\rm var}\left\{\lambda(x)\mid\eta\right\} &=\sum_{n=1}^\infty \left\{\frac{1}{\eta}\left(1-\frac{1}{\eta}\right)^n \frac{nx(2- x + nx)}{(n+1)(n+2)}\right\} - E\left\{\lambda(x)\mid\eta\right\}^2\!\text{.} \end{align*} Hence, the expectation is a linear function whose slope depends on $$\eta$$. See the Supplementary Material for the the proof of Proposition 1. This Bayesian framework has one small limitation. If, for instance, $$X=[0,1]$$, then $$\lambda(0)=\delta_{\min}$$ almost surely. To address this, we define $$\lambda\left(x\right)=\mu + \varphi\left(x\right)$$, where $$\varphi: X\to [\delta_{\min},\delta_{\max}]$$ is monotonic and $$\mu\in\mathbb{R}$$, and with priors $$\tilde{\phi}(\varphi\mid\eta)$$ and $$\pi(\mu)$$, respectively. A second approach is presented in the Supplementary Material. 2.4. Combining the spatial dependence model and marked point process prior We now impose a spatial structure on the $$K$$ sets of marked point processes $$\Delta_1,\ldots,\Delta_K$$, $$\Delta_k = \left(\Delta_{k,1},\ldots,\Delta_{k,I}\right)$$ ($$k=1,\ldots,K$$), by combining $$\phi\left(\Delta_k\mid\eta\right)$$ in (6) with $$\pi\left(\lambda_1,\ldots,\lambda_K\mid\omega\right)$$ in (4). The joint prior $$\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega, \eta\right)$$ is then proportional to $$\prod_{1\leqslant k<k'\leqslant K} \exp\left[-\omega\,d_{k,k'}\,\int_{X}\gamma_{p}\left\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\right\}\,\mbox{d}x\right] \times \prod_{k=1}^K \phi\left(\Delta_k\mid \eta\right)\!,$$ (7) where $$\tilde\lambda_k$$ and $$\tilde\lambda_{k'}$$ are the step functions represented by $$\Delta_k$$ and $$\Delta_{k'}$$, respectively. Since $$\tilde\lambda_1,\ldots,\tilde\lambda_K$$ are step functions, the integral in (4) simplifies to a sum and can be computed efficiently. The full conditional prior density for $$\Delta_k$$ in (7) converges to (6) as $$\omega\to0$$. Further, $$\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega, \eta\right)$$ is proper because $$\pi(\tilde\lambda_1,\ldots,\tilde\lambda_K\mid\omega)$$ lies within $$(0,1]$$ and $$\phi(\Delta_k\mid\eta)$$ is a proper density function. The likelihood (1) and prior (7) specify a posterior distribution for $$\Delta_1,\ldots,\Delta_K$$ with density $$\pi\left(\Delta_1, \ldots,\Delta_K \mid \mathcal{D}, \omega,\eta\right)\propto \left[\prod_{k=1}^K \prod_{t=1}^{T_k} f\left\{\,y_{k,t}\mid\tilde\lambda_k\left(x_{k,t}\right)\!,\theta_k\right\}\right] \times\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega,\eta\right)\!,$$ (8) where $$\mathcal{D}$$ denotes the data and $$T_k$$ is the number of observations for region $$k$$ ($$k=1,\ldots,K$$). An estimator should be consistent. In Bayesian nonparametrics, consistency is often considered in terms of the Hellinger distance. Let $$\left(\lambda_k,\theta_k\right)$$ denote the true model parameters for region $$k$$ ($$k=1,\ldots,K$$) and let $$G_k$$ be the distribution of the explanatory variables, $$x_k\sim G_k$$. Following Walker & Hjort (2001), we denote the Hellinger distance between the densities with parameters $$(\lambda_k, \theta_k)$$ and $$(\tilde{\lambda}_k,\tilde{\theta}_k)$$ by $$H_k\left(\tilde\lambda_k,\tilde\theta_k\right) = \left(1 - \int \int \left[f\left\{\,y\mid\tilde\lambda_k(x_k),\tilde\theta_k\right\} f\left\{\,y\mid\lambda_k(x_k),\theta_k\right\}\right]^{1/2} \mbox{d}y G_k(\mbox{d}x_k)\right)^{1/2}\!\text{.}$$ (9) Let $$\Lambda = \left(\lambda_1,\ldots,\lambda_K\right)$$ and $$\Theta = \left(\theta_1,\ldots,\theta_K\right)$$. We then define a neighbourhood $$U_{\epsilon}\left(\Lambda, \Theta\right)$$ around the truth $$\left(\Lambda, \Theta\right)$$ with respect to $$H_1,\ldots,H_K$$ in (9) by $U_{\epsilon}\left(\Lambda, \Theta\right) = \left\{\left(\tilde{\Lambda},\tilde{\Theta}\right) {\ :\ } H_k\left(\tilde\lambda_k,\tilde{\theta}_k\right) \leqslant \epsilon, k=1,\ldots,K\right\}\!,\qquad\epsilon>0\text{.}$ Here, $$U_{\epsilon}\left(\Lambda, \Theta\right)$$ contains only step functions and is nonempty because we can approximate $$\lambda_k$$ by a step function to any degree of accuracy. In the following, we focus on $$f\left\{\,y_k\mid \lambda_k\left(x_k\right),\theta_k\right\}$$ being the normal density function with mean $$\lambda_k\left(x_k\right)$$ and variance $$\theta_k$$, but the theory can be generalized and holds for all examples in this paper. Theorem 1. Let $$G_k$$ ($$k=1,\ldots,K$$) be absolutely continuous and assign positive mass to any nondegenerate subset of $$X$$. Further, let the prior $$\pi(\tilde\Theta)$$ put positive mass on any neighbourhood of $$\Theta$$. Then, for $$\lambda_1,\ldots,\lambda_K:X \to \left[\delta_{\min},\delta_{\max}\right]$$ monotonic and continuous and for $$\epsilon>0$$, $$\tilde\Pi\left\{U_{\epsilon}^c\left(\Lambda,\Theta\right)\mid\mathcal{D}, \omega, \eta \right\} \to 0$$ almost surely as $$\min_{k=1,\ldots,K} T_k \to \infty$$. Here, $$U_{\epsilon}^c\left(\Lambda,\Theta\right)$$ is the complement of $$U_{\epsilon}\left(\Lambda,\Theta\right)$$ and $$\tilde\Pi$$ denotes the posterior distribution induced by the likelihood (1) and the priors $$\pi(\tilde\Theta)$$ and $$\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega, \eta\right)$$. Hence, the posterior distribution concentrates around the $$K$$ true functions as the number of data points becomes large, conditional on appropriate boundaries $$\delta_{\min}$$ and $$\delta_{\max}$$. Moreover, the posterior mean may be smooth, as the model permits variability in the number, locations and heights of the steps. Consequently, our approach can recover both smooth and discontinuous functional shapes. This result is well known for the estimation of a single probability density function using a piecewise approximation (Heikkinen & Arjas, 1998). The proof of Theorem 1 is in the Supplementary Material. In a fully Bayesian framework, we would need priors for $$\eta$$ and $$\omega$$. However, the normalizing constant of $$\pi(\Delta_1,\ldots,\Delta_K\mid\omega,\eta)$$ in (7) is intractable, unless $$\omega=0$$. This leads to our novel inferential approach for $$\omega$$ in § 2.6. In terms of setting $$\eta$$, Proposition 1 implies that higher values of $$\eta$$ will generally lead to smoother surfaces. Alternatively, one may learn about $$\eta$$ by considering the case $$\omega=0$$. We can then specify a conjugate beta prior for $$1/\eta$$ and sample from the full conditional beta posterior; the performance of this approach is explored in § 3. 2.5. Inference and analysis of the marked point processes Our scheme to sample from the posterior density in (8) is based on Saarela & Arjas (2011). Initially, $$\Delta_{k,i}$$ ($$k=1,\ldots,K; i=1,\ldots,I$$) is empty and so $$\lambda_k=\delta_{\min}$$. The $$K$$ sets are then updated sequentially. We first select one of the processes $$\Delta_{k,1},\ldots,\Delta_{k,I}$$ ($$k=1,\ldots,K$$) with equal probability. For the sampled process $$\Delta_{k,i^*}$$, we randomly propose one of three moves, implying local changes of $$\lambda_k$$. A birth move adds a point $$\left(\xi^*,\delta^*\right)$$ to $$\Delta_{k,i^*}$$, where $$\xi^*$$ is sampled uniformly on $$X_{i^*}$$. Given $$\xi^*$$, the associated mark $$\delta^*$$ is sampled uniformly, subject to monotonicity being preserved. A death move removes a point from $$\Delta_{k,i^*}$$, maintaining reversibility. A shift move changes the location and mark of a point in $$\Delta_{k,i^*}$$, subject to the partial order imposed by the monotonicity constraints being maintained. See the Appendix for details and the acceptance probabilities. We implemented this scheme in C++, and a simulation study to verify correctness is provided in the Supplementary Material. Realizations sampled from the posterior distribution are rich and facilitate detailed analysis of $$\lambda_1,\ldots,\lambda_K$$. Thinning of the Markov chains is needed to reduce autocorrelation. Posterior mean estimates for $$\lambda_k$$ are obtained by averaging over the stored realizations. The mean and quantiles of the posterior distribution are accessible for any $$x\in X$$ by deriving $$\lambda_k(x)$$ for each sample. Further, the samples facilitate the detection of discontinuities; see the Supplementary Material. 2.6. Estimation of $$\omega$$ The performance of our approach relies on a suitable $$\omega$$ in (7). If $$\omega$$ is too high, spatial variation is oversmoothed, while overfitting may occur if $$\omega$$ is too small. Since the normalizing constant of (7) is intractable, we cannot sample from the full conditional distribution of $$\omega$$ via an additional Gibbs step within the scheme in § 2.5. Further, while there exists a rich literature on handling intractable normalizing constants (Beaumont et al., 2002; Møller et al., 2006; Andrieu & Roberts, 2009), these approaches cannot be adapted for use here since efficient sampling from the prior distribution in (7) is infeasible. Hence, we estimate $$\omega$$ prior to inference on $$\Delta_1,\ldots,\Delta_K$$. One approach is $$s$$-fold crossvalidation: the data for each of the $$K$$ regions are split into $$s$$ subsets of equal size. The sampling scheme in § 2.5 is then performed $$s$$ times with varying training and test data. Parameter values are compared by the posterior mean squared error for the test data points. In order to keep the number of evaluated values for $$\omega$$ small, we combine crossvalidation with the global optimization algorithm of Jones et al. (1998). Efficient global optimization postulates a sequential design strategy to detect global extrema of a black-box function $$r$$. The algorithm is widely applied in simulations if $$r$$ is costly to evaluate and the parameter space $$Z$$ is small (Roustant et al., 2012). The rationale is to model $$r$$ by a Gaussian process $$R$$ which is updated sequentially. Specifically, the proposal $$z^*\in Z$$ is selected to maximize the expected improvement $$E\left[\max\left\{r_{\rm opt}-R(z),0\right\}\right],\qquad z\in Z,$$ (10) where $$r_{\rm opt}$$ denotes the current optimum. Hence, (10) represents the potential of $$r(z)$$ to be smaller than $$r_{\rm opt}$$. The proposal is evaluated until its expected improvement falls below a critical value, corresponding to $$r_{\rm opt}$$ being sufficiently close to the unknown minimum of $$r$$. As this approach balances local exploration of the areas likely to provide good model fit and a global search, a suitable solution is generally found after a reasonable number of evaluations. When estimating $$\omega$$, interest lies in the minimum of the crossvalidation function CV($$\omega$$). Algorithm 1 sketches our approach. We first derive an upper bound as efficient global optimization can only be applied to a closed set. An initial bound $$\omega_u$$ is increased until its mean squared error is greater than that for $$\omega=0$$ by a sufficient amount; $$\beta=2$$ in Algorithm 1 proved reasonable in our simulations. Once $$\omega_u$$ is fixed, an initial proposal $$\omega^*\in\left[0, \omega_u\right]$$ is made, guaranteeing that $$R$$ in (10) is fitted with at least three data points. We use the DiceOptim R package (Roustant et al., 2012) to derive the expected improvement and run multiple $$s$$-fold crossvalidations with the same $$\omega$$ to reduce the dependence on the split of the data. The mean and variance of the mean squared error across the repetitions are used to fit $$G$$. We then repeatedly perform crossvalidation and update $$\omega^*$$ until the maximum expected improvement falls below the critical value $$\alpha$$. To conclude, we set $$\omega$$ to the value $$\omega_{\rm opt}$$ that provided the lowest mean squared error. Algorithm 1. Combination of efficient global optimization and crossvalidation. Set initial upper bound $$\omega_u$$, critical value $$\alpha$$ and factor $$\beta$$ Perform crossvalidation for $$\omega=0$$ and store CV$$(0)$$ While CV$$(\omega_u) {\ <\ } \beta$$CV$$(0)$$ Increase $$\omega_u$$ Perform crossvalidation for $$\omega_u$$ and store CV$$(\omega_u)$$ Set initial proposal $$\omega^*$$, e.g. $$\omega^* = \omega_u/2$$ Initialize maximum expected improvement $$M>\alpha$$ While $$M>\alpha$$ Perform crossvalidation for $$\omega^*$$ and store CV$$(\omega^*)$$ Fit Gaussian process $$R$$ Update $$\omega^*$$ and $$M$$ Return value $$\omega_{\rm opt}$$ which provided smallest error 3. Simulation study 3.1. Introduction We aim to demonstrate that our method improves estimates if similarities between functions exist, and is robust otherwise. Furthermore, we examine sensitivity to the prior parameters $$p$$ and $$\eta$$ in expression (7). Responses for region $$k$$ ($$k=1,\ldots,K$$) are simulated independently from \begin{equation*} y_k\mid x_k\sim {N}\left\{\lambda_k(x_k), \theta_k\right\}\!, \end{equation*} where $$x_k\in[0,1]^2$$. As described in § 2.3, we define $$\lambda_k(x) = \mu_k + \varphi_k(x)$$ and perform inference on $$\mu_k\in\mathbb{R}$$ and $$\varphi_k:[0,1]^2\to\left[\delta_{\min},\delta_{\max}\right]$$. The likelihood (1) is then \begin{equation*} f\left\{\,y_k \mid \varphi_k\left(x_k\right)\!, \mu_k, \theta_k\right\} = \left(\frac{1}{2\pi\theta_k}\right)^{1/2}\exp\left[-\frac{1}{2\theta_k}\left\{\,y_k-\mu_k-\varphi_k(x_k)\right\}^2\right]\!\text{.} \end{equation*} An intrinsic conditional autoregressive prior (Besag et al., 1991; Rue & Held, 2005) is defined for $$(\mu_1,\ldots,\mu_K)$$ and imposes a spatial structure. Here, $$\mu_1,\ldots,\mu_K$$ are updated separately via a random walk Metropolis step and the hyperparameter in $$\pi\left(\mu_1,\ldots,\mu_K\right)$$ is updated via Gibbs sampling (Knorr-Held, 2003). Furthermore, we assign the prior distribution $$1/\theta_k \sim \mbox{Ga}\left(1, 0{\cdot}001\right)$$ ($$k=1,\ldots,K$$) and update $$\theta_1,\ldots,\theta_K$$ via Gibbs sampling. Here, $$X$$ is the square spanned by the minimum and maximum observed values in each explanatory variable across the $$K$$ regions. The boundaries are set to $$\delta_{\min} = -1$$ and $$\delta_{\max}=4$$. We assess performance via the absolute difference of the posterior mean estimate $$\hat{\lambda}_k$$ and the true function $$\lambda_k$$, over a regular $$100\times 100$$ grid on $$X$$. Only grid points contained in the convex hull of the observed values of $$x_k$$ ($$k=1,\ldots,K$$) are considered. Improvements are discussed with respect to the setting $$\omega=0$$, which imposes no dependence. Algorithm 1 is applied with $$\beta=2, \alpha=\,$$$${\small{\text{CV}}}(0)/1000$$ and $$\omega_u=50$$. We increase $$\omega_u$$ by a factor of 10 until CV($$\omega_u$$) $$<$$ 2 CV(0). For each proposed $$\omega$$, five repetitions of 10-fold crossvalidation are performed. A fold consists of 50 000 iterations and every 100th sample is stored after a burn-in period of 25 000 iterations. In addition to the expected improvement criterion, we stop if 30 proposals have been considered; this occurred once in all our simulations. Birth, death and shift moves are proposed with probabilities 0$${\cdot}$$3, 0$${\cdot}$$3 and 0$${\cdot}$$4. Estimates for $$\Delta_1,\ldots,\Delta_K$$ are based on 3 000 000 iterations, with the first 1 000 000 discarded and then every 1000th sample stored. Convergence of the sampled Markov chains for $$\Delta_k$$ ($$k=1,\ldots,K$$) is checked via the trace plots of $$\lambda_k(x)$$ for ten random points in $$X$$. Posterior mean plots and trace plot examples are provided in the Supplementary Material. We also applied our method to non-Gaussian settings; an example with binomial response data is presented in the Supplementary Material. The C++ and R code for all simulations is provided in the Supplementary Material. 3.2. Sensitivity analysis on $$\eta$$ We explore general performance and sensitivity to $$\eta$$ based on five simulations with $$K=2$$ regions. Columns 1 and 2 in Fig. 3 illustrate the five pairs of $$(\lambda_1,\lambda_2)$$. Across all studies, $$\lambda_k\left(x_k\right)\in\left[0,2\right]$$ ($$k=1,2$$). For each study, 1000 and 100 data points are sampled for regions 1 and 2, respectively, with $$\theta_k=0{\cdot}05^2$$ and $$x_k\sim\mbox{Un}([0,1]^2)$$ ($$k=1,2$$). This setting explores the potential benefits of borrowing statistical information from region 1 when estimating $$\lambda_2$$. We fix the prior parameter $$p=1$$ and consider three settings for $$\eta$$: (i) $$\eta=10$$, (ii) $$\eta=1000$$ and (iii) $$\eta=\hat{\eta}$$. Here, $$\hat{\eta}$$ is the posterior mean estimate for $$\eta$$ in the case $$\omega=0$$ as described in § 2.4. Fig. 3. View largeDownload slide True functions $$\lambda_1$$ (column 1) and $$\lambda_2$$ (column 2) and the posterior mean estimate $$\hat\lambda_2$$ obtained for $$\eta= 1000$$(Column 3) for the simulations in § $$3.2$$. Fig. 3. View largeDownload slide True functions $$\lambda_1$$ (column 1) and $$\lambda_2$$ (column 2) and the posterior mean estimate $$\hat\lambda_2$$ obtained for $$\eta= 1000$$(Column 3) for the simulations in § $$3.2$$. We also estimate a monotonized generalized additive model for each region separately and derive the same summary statistics as for our approach. We first fit a generalized additive model (Hastie & Tibshirani, 1990) and then apply the projection by Lin & Dunson (2014); plots of the estimated surfaces are provided in the Supplementary Material. Studies 1 and 2 consider the case $$\lambda_1 = \lambda_2$$ and Table 1 shows reduced error measures, particularly for region 2, compared to the setting where $$\omega=0$$. Figure 3 illustrates that both smooth surfaces and discontinuities are recovered well. In Study 3 and Study 4, $$\lambda_1$$ and $$\lambda_2$$ are similar and the conclusions are consistent with those for Study 1 and Study 2. Study 5 considers the case of $$\lambda_1$$ being smooth while $$\lambda_2$$ is piecewise linear. Table 1 shows no worsening in the error measures, demonstrating robustness of our method. The prospect of variable selection described in § 2.3 has been examined for $$\lambda_2$$ in Study 5, where $$\lambda_2(x)$$ depends only on $$x_{2,1}$$. Almost all sampled points are in $$\Delta_{2,1}$$; hence the results indicate $$x_{2,2}$$ to be redundant. Table 1. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2)$$ in Studies 1 to 5 in Fig. 3 for $$\eta=(10,1000,\hat{\eta})$$, $$\omega=0$$ and GAM, an estimated monotonized generalized additive model Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Table 1. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2)$$ in Studies 1 to 5 in Fig. 3 for $$\eta=(10,1000,\hat{\eta})$$, $$\omega=0$$ and GAM, an estimated monotonized generalized additive model Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Table 1 shows that our approach performs better than the fitted monotonized generalized additive model, unless the true function is smooth. The results also indicate a low sensitivity to $$\eta$$. In particular, higher values of $$\eta$$ yield improved results if the true function is smooth, as in Study 3, or requires a large number of points to be approximated, as in Study 4. Conversely, $$\eta=10$$ performs better in Study 1 and Study 2, as it does not tend to interpolate linearly when functions switch between a zero and nonzero slope. These findings are consistent with § 2: a higher value for $$\eta$$ tends to produce smoother estimates, as the sampled functions have more but smaller steps. 3.3. Sensitivity analysis on $$p$$ We consider $$K=3$$ regions with region 2 adjacent to regions 1 and 3 while region 1 and 3 are nonadjacent. Figure 4 shows the true functions $$(\lambda_1,\lambda_2, \lambda_3)$$, which all exhibit a discontinuity at $$(0{\cdot}5, 0{\cdot}5)$$ and are more similar for $$x_k\in[0,1]^2\setminus[0{\cdot}5,1{\cdot}0]^2$$ than for $$x_k\in[0{\cdot}5,1{\cdot}0]^2$$ ($$k=1,2,3$$). The distribution of $$x_k$$ ($$k=1,2,3$$) varies across three studies while $$\lambda_1,\lambda_2$$ and $$\lambda_3$$ remain unchanged. Specifically, the studies explore the performance of our approach, subject to the relative intensity of points in subsets of $$X$$ for which the functions are similar. Fig. 4. View largeDownload slide True functions $$(\lambda_1,\lambda_2,\lambda_3$$) in § 3.3. The $$\lambda_k(x)$$-axis ($$k=1,2,3$$) is from 0 to 3. Fig. 4. View largeDownload slide True functions $$(\lambda_1,\lambda_2,\lambda_3$$) in § 3.3. The $$\lambda_k(x)$$-axis ($$k=1,2,3$$) is from 0 to 3. We generate 200 data points for each region with variance $$\theta_k=0{\cdot}2^2$$ ($$k=1,2,3$$). The three studies vary with respect to the number of observations sampled on $$[0{\cdot}5,1{\cdot}0]^2$$ for regions 1 and 3, while $$x_2\sim\mbox{Un}([0,1]^2)$$ in all of them. Study 1 considers the case where $$x_k\sim\mbox{Un}([0,1]^2)$$ ($$k=1,3$$). In Study 2, 150 data points are sampled uniformly from $$[0{\cdot}5,1{\cdot}0]^2$$ for regions 1 and 3, while only 25 observations are sampled from this subset in Study 3. The remaining 175 and 50 data points in Study 2 and Study 3, respectively, are sampled uniformly from $$[0{\cdot}0,1{\cdot}0]^2\setminus[0{\cdot}5,1{\cdot}0]^2$$. We compare four settings for $$p$$. The first, $$p=1$$, yields the integrated squared difference in expression (2). Settings $$p=0{\cdot}2$$ and $$p=0{\cdot}6$$ allow for stronger dependence in the lower function values, while $$p=2$$ imposes increased dependence for higher function values. The other parameters are fixed at $$\eta=1000$$, $$d_{1,2}=d_{2,3}=1$$ and $$d_{1,3}=0$$. Table 2 shows that we improve upon $$\omega=0$$, except for $$p=2$$ in Study 3, and indicates sensitivity to the prior parameter $$p$$, as the settings with $$p<1$$ perform best. Since a setting of $$p<1$$ imposes higher dependence on the lower function values, we effectively borrow information across the functions in Fig. 4 to improve estimates for the lower function values without inducing a large bias on the upper function values. As such, our extended discrepancy measure based on (3) has benefits when compared to the integrated squared distance. Table 2 further indicates that the sensitivity to $$p$$ depends on where most of the data are observed: if data fall in areas where the functions differ, the sensitivity is lower. The individual summary statistics for each function are provided in the Supplementary Material. Table 2. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2,\lambda_3)$$ in Fig. 4 for the settings $$p=(1{\cdot}0, 0{\cdot}2, 0{\cdot}6, 2{\cdot}0)$$ and $$\omega=0$$ Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) Table 2. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2,\lambda_3)$$ in Fig. 4 for the settings $$p=(1{\cdot}0, 0{\cdot}2, 0{\cdot}6, 2{\cdot}0)$$ and $$\omega=0$$ Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) 4. Case study We consider the Norwegian insurance and weather data used by Haug et al. (2011) and Scheel et al. (2013). The data provide the daily number of insurance claims due to precipitation, surface water, snow melt, undermined drainage, sewage back-flow or blocked pipes at municipality level from 1997 to 2006. The average number of policies held per month and multiple daily weather metrics, such as the amount of precipitation, are also recorded. Table 2 in Scheel et al. (2013) indicates that a generalized linear model underpredicts high numbers of claims, perhaps due to threshold effects, as the risk of localized flooding only exists for high daily precipitation levels. While linearity may be too strong an assumption, the risk per property increases with precipitation levels, motivating the application of our method. We consider the $$K=11$$ municipalities in Fig. 5 and explore the effect of the precipitation $$R_{k,t}$$ and $$R_{k,t-1}$$ ($$k=1,\ldots,11$$) on day $$t$$ and $$t-1$$, as Haug et al. (2011) and Scheel et al. (2013) find these to be the most informative explanatory variables. Fig. 5. View largeDownload slide Map of the Norwegian municipalities in § 4. Fig. 5. View largeDownload slide Map of the Norwegian municipalities in § 4. Let $$N_{k,t}$$ and $$A_{k,t}$$ denote the numbers of claims and policies, respectively, on day $$t$$ for municipality $$k$$. We model $$N_{k,t}$$ as binomial with the logit of the daily claim probability, $$p_{k,t}$$, given by $$\lambda_k\left(R_{k,t},R_{k,t-1}\right)$$. As in § 3, we define $$\lambda_k\left(R_{k,t}, R_{k,t-1}\right) = \mu_k + \varphi_k\left(R_{k,t},R_{k,t-1}\right)$$ and estimate $$\mu_k$$ and $$\varphi_k$$ ($$k=1,\ldots,11$$). Formally, \begin{equation*} N_{k,t} \sim \mbox{Bi}\left(A_{k,t}, p_{k,t}\right)\!,\qquad\mbox{logit} p_{k,t} = \mu_k + \varphi_k\left(R_{k,t},R_{k,t-1}\right)\!\text{.} \end{equation*} An intrinsic conditional autoregressive prior is defined for $$\mu_1, \ldots, \mu_{11}$$, and the boundaries of $$\varphi_k$$ ($$k=1,\ldots,11$$) are set to $$\delta_{\min}=0$$ and $$\delta_{\max}=10$$. The set $$X$$ is derived as the square spanned by the observed minima and maxima of $$R_{k,t}$$ across all municipalities and years. We set $$d_{k,k'}=1$$ in (4) if municipalities $$k$$ and $$k'$$ are adjacent and $$d_{k,k'}=0$$ otherwise. To avoid oversmoothing threshold effects, we select $$\eta=10$$, based on our results in § 3.2. The sensitivity analysis in § 3.3 motivates setting $$p<1$$ since we expect the municipalities to exhibit similar vulnerability to small amounts of precipitation, while differences in infrastructure, for example, may lead to different effects for higher precipitation levels. We set $$p=0{\cdot}5$$. The functions $$\lambda_1,\ldots,\lambda_{11}$$ are estimated based on 1 000 000 iteration steps,with every 500th sample stored after a burn-in period of 200 000 iterations. To assess predictive performance, observations for 2001 and 2003 are stored as test data and $$\lambda_1,\ldots,\lambda_{11}$$ are estimated from the remaining eight years. We consider two competing models: (i) the average daily number of claims in the municipality over the training period; (ii) a linear model with spatially varying parameters (Assunção, 2003). The latter is estimated via 10 000 iterations of a random walk Metropolis scheme, with the first 1000 samples discarded. Table 3 shows that our approach is the best in terms of overall predictive performance. The small scale of improvement from $$\omega=0$$ to $$\omega=\omega_{\rm opt}$$ is due to the large number of training data points; important structures in $$\lambda_1,\ldots,\lambda_{11}$$ are likely to be captured without borrowing statistical information from adjacent municipalities. Posterior mean plots for Oslo and Hurum are provided in the Supplementary Material, but the function values are omitted for confidentiality. Table 3. Sum of squared errors of the daily number of claims in 2001 and 2003 for four models, with estimates being based on the remaining eight years between 1997 and 2006 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 Table 3. Sum of squared errors of the daily number of claims in 2001 and 2003 for four models, with estimates being based on the remaining eight years between 1997 and 2006 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 The largest improvement is achieved for Bærum, which has the highest $$N_{k,t}$$ over the test period. Hence, the increased flexibility of our approach captures the dynamics leading to large numbers of claims better than competing models. For the other municipalities, the models perform similarly, due to there being zero high-claim days over the test period. This is also indicated by the predictive error of the constant mean model being low for most municipalities. Our approach performs slightly worse than the linear model for Asker, which is due to a single observation $$N_{k,t}$$. In particular, high precipitation levels caused a count $$N_{k,t}$$ which was the highest over the full 10-year period. 5. Discussion Our modelling framework can be extended to a spatio-temporal context. Assume that the intercept changes between observations but the effect of the explanatory variables is temporally stationary. We can then define $$\lambda_{k,t}\left(x\right) = \mu_{k,t} + \varphi_{k}\left(x\right)$$ ($$k=1,\ldots,K$$), similar to § 3 and § 4. Temporal structure on $$\mu_{k,1},\ldots,\mu_{k,T_k}$$ is, for instance, imposed via an autoregressive model. Our approach can also be extended to a setting for which $$\lambda_1,\ldots,\lambda_K$$ change at specified time-points, with temporal structure being imposed analogously to the spatial structure using time-adjacency. An aspect not discussed is the selection of the number $$I$$ of marked point processes representing $$\lambda_k$$ ($$k=1,\ldots,K$$). Since we considered examples with $$m=2$$ explanatory variables, $$I=3$$. In higher dimensions, however, one may want to restrict $$I$$. Assume there exists prior knowledge that continuous variable $$x_{k,h}$$ ($$h=1,\ldots,m$$) is informative and let $$X=[0,1]^m$$. The set of processes could then be defined based on the nonempty subsets of $$\left\{1,\ldots,m\right\}$$ which contain $$h$$. Consequently, we would represent $$\lambda_k$$ ($$k=1\ldots,K$$) via $$2^{m-1}$$, instead of $$2^{m}-1$$, processes. Our method performs well for regression problems with $$m=2$$ to $$m=5$$ explanatory variables. However, as for other flexible approaches, such as generalized additive models, some issues arise in higher dimensions. Firstly, the computational cost of calculating the prior ratio grows exponentially with $$m$$. We reduce this cost by deriving the subset of $$X$$ affected by the proposal before evaluating the integral in expression (4). Secondly, the monotonicity constraint becomes less restrictive with increasing dimension, leading to potential overfitting. Larger sets of explanatory variables can be accommodated by imposing an additive or semiparametric structure on $$\lambda_k$$ ($$k=1,\ldots,K$$), where the lower-dimensional monotonic functions are then estimated jointly. Consequently, our method can be applied to higher-dimensional regression problems, but we would recommend a pre-analysis. Our work can be extended in several ways, such as to the construction of other discrepancy measures based, for instance, on the Kullback–Leibler divergence. When estimating $$\omega$$, parallelized computing techniques, allocating the folds to multiple processors, can reduce the computational time. Further, we arbitrarily fixed the number of folds to $$s=10$$, but the value for $$\omega$$ also depends on the number of data points. A larger number of folds may return a more robust estimate. Acknowledgement Rohrbeck gratefully acknowledges funding from the U.K. Engineering and Physical Sciences Research Council. This work was also financially supported by the Norwegian Research Council. We greatly benefited from discussions with colleagues at Lancaster University, Elija Arjas and Sylvia Richardson. We also thank Ida Scheel for providing access to the insurance and weather data, and the editors and two referees for suggestions that substantially improved the presentation of the paper. Supplementary material Supplementary material available at Biometrika online contains a dependence model for functions with varying support, details on the prior and the sampling scheme, the proofs of Proposition 1 and Theorem 1, an algorithm to detect discontinuities, further material related to the analysis in § 3 and 4 and additional simulation studies. Appendix Details of the sampling scheme for the marked point processes We present the acceptance probabilities for the three moves in § 2.5; more details are provided in the Supplementary Material. For notational simplicity, let birth and death moves be proposed with equal probability and let $$\Delta_{k,i^*}$$ ($$k=1,\ldots,K; i^*=1,\ldots,I$$) denote the marked point process to be updated. A birth move proposes the addition of a point $$\left(\xi^*, \delta^*\right)$$ to $$\Delta_{k,i^*}$$. Since this increases the dimension of the parameter space, the acceptance probability has to be derived as described by Green (1995). The mapping for adding a point is equal to the identity function and, hence, the determinant of the Jacobian in the acceptance probability is equal to 1. Further, the proposal densities $$q(\xi^*)$$ and $$q\left(\delta^*\mid\xi^*,\Delta_k\right)$$ cancel with parts of the prior $$\phi\left(\Delta_k\mid\eta\right)$$. Formally, the acceptance probability is \begin{equation*} \min\!\!\left(\!1,\prod_{t=1}^{T_k}\frac{f\left\{\,y_{k,t}\mid \lambda_k^*(x_{k,t}),\theta_k\right\}}{f\left\{\,y_{k,t}\mid \lambda_k(x_{k,t}),\theta_k\right\}} \times \prod_{k'\neq k} \frac{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda^*_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} {\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} \times \left(1-\frac{1}{\eta}\right)\frac{N_k+1}{N_k+I}\!\right)\!\text{.} \end{equation*} A death or shift move is rejected if $$\Delta_{k,i^*}$$ contains no points. Otherwise, a death move selects one of the $$n_{k,i^*}$$ existing points with equal probability and proposes to remove it. The acceptance probability for a death move is then \begin{equation*} \min\!\!\left(\!1,\prod_{t=1}^{T_k}\frac{f\big\{\,y_{k,t}\mid \tilde\lambda_k^*(x_{k,t}),\theta_k\big\}}{f\big\{\,y_{k,t}\mid \tilde\lambda_k (x_{k,t}),\theta_k\big\}} \times \prod_{k'\neq k} \frac{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda^*_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} {\exp\big[{-}\omega d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} \times\frac{1}{1-\frac{1}{\eta}}\frac{N_k+I-1}{N_k}\!\right)\!\text{.} \end{equation*} Finally, a shift move changes both the location and mark of an existing point, subject to the partial ordering of the locations in $$\Delta_{k,1},\ldots,\Delta_{k,I}$$, induced by the monotonicity constraint, being maintained. First, one of the $$n_{k,i^*}$$ points in $$\Delta_{k,i^*}$$ is selected with equal probability. The proposed location $${\xi}^*$$ is then sampled uniformly on the subset of $$X_i$$ which maintains the total order in each component of the locations; see Saarela & Arjas (2011) for details. The proposed mark $$\delta^*$$ is then sampled uniformly, subject to the monotonicity constraints. Formally, the acceptance probability is \begin{equation*} \min\left(1, \prod_{t=1}^{T_k}\frac{f\big\{\,y_{k,t}\mid\tilde\lambda_k(x_{k,t}),\theta_k\big\}}{f\big\{\,y_{k,t}\mid\tilde\lambda_k(x_{k,t}),\theta_k\big\}} \times \prod_{k'\neq k} \frac{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]}{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x) \big\}\,\mbox{d}x\big]}\right)\!\text{.} \end{equation*} References Andrieu C. & Roberts G. O. ( 2009 ). The pseudo-marginal approach for efficient Monte Carlo computations. Ann. Statist. 37 , 697 – 725 . Google Scholar CrossRef Search ADS Assunçço R. M. ( 2003 ). Space varying coefficient models for small area data. Environmetrics 14 , 453 – 73 . Google Scholar CrossRef Search ADS Ayer M., Brunk H. D., Ewing G. M., Reid W. T. & Silverman E. ( 1955 ). An empirical distribution function for sampling with incomplete information. Ann. Math. Statist. 26 , 641 – 7 . Google Scholar CrossRef Search ADS Bacchetti P. ( 1989 ). Additive isotonic model. J. Am. Statist. Assoc. 84 , 289 – 94 . Barlow R. & Brunk H. ( 1972 ). The isotonic regression problem and its dual. J. Am. Statist. Assoc. 67 , 140 – 7 . Google Scholar CrossRef Search ADS Barron A., Schervish M. J. & Wasserman L. ( 1999 ). The consistency of posterior distributions in nonparametric problems. Ann. Statist. 27 , 536 – 61 . Google Scholar CrossRef Search ADS Beaumont M. A., Zhang W. & Balding D. J. ( 2002 ). Approximate Bayesian computation in population genetics. Genetics 162 , 2025 – 35 . Google Scholar PubMed Bell M. L., McDermott A., Zeger S. L., Samet J. M. & Dominici F. ( 2004 ). Ozone and short-term mortality in 95 US urban communities, 1987–2000. J. Am. Med. Assoc. 292 , 2372 – 8 . Google Scholar CrossRef Search ADS Bergersen L. C., Tharmaratnam K. & Glad I. K. ( 2014 ). Monotone splines lasso. Comp. Statist. Data Anal. 77 , 336 – 51 . Google Scholar CrossRef Search ADS Besag J., York J. & Mollié A. ( 1991 ). Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Statist. Math. 43 , 1 – 20 . Google Scholar CrossRef Search ADS Bowman A. W. & Azzalini A. 1997 . Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations . Oxford : Clarendon Press . Bowman A. W., Jones M. C. & Gijbels I. ( 1998 ). Testing monotonicity of regression. J. Comp. Graph. Statist. 7 , 489 – 500 . Brunk H. D. ( 1955 ). Maximum likelihood estimates of monotone parameters. Ann. Math. Statist. 26 , 607 – 16 . Google Scholar CrossRef Search ADS Brunk H. D., Ewing G. M. & Utz W. R. ( 1957 ). Minimizing integrals in certain classes of monotone functions. Pac. J. Math. 7 , 833 – 47 . Google Scholar CrossRef Search ADS Cahill M. & Mulligan G. ( 2007 ). Using geographically weighted regression to explore local crime patterns. Social Sci. Comp. Rev. 25 , 174 – 93 . Google Scholar CrossRef Search ADS Congdon P. ( 2006 ). A model for non-parametric spatially varying regression effects. Comp. Statist. Data Anal. 50 , 422 – 45 . Google Scholar CrossRef Search ADS Fang Z. & Meinshausen N. ( 2012 ). LASSO isotone for high-dimensional additive isotonic regression. J. Comp. Graph. Statist. 21 , 72 – 91 . Google Scholar CrossRef Search ADS Farah M., Kottas A. & Morris R. D. ( 2013 ). An application of semiparametric Bayesian isotonic regression to the study of radiation effects in spaceborne microelectronics. Appl. Statist. 62 , 3 – 24 . Fotheringham A. S., Brunsdon C. & Charlton M. 2003 . Geographically Weighted Regression: The Analysis of Spatially Varying Relationships . Hoboken, New Jersey : Wiley . Gelfand A. E. & Kuo L. ( 1991 ). Nonparametric Bayesian bioassay including ordered polytomous response. Biometrika 78 , 657 – 66 . Google Scholar CrossRef Search ADS Georgii H.-O. 2011 . Gibbs Measures and Phase Transitions . Berlin : de Gruyter , 2nd ed. Ghosal S., Sen A. & van der Vaart A. W. ( 2000 ). Testing monotonicity of regression. Ann. Statist. 28 , 1054 – 82 . Google Scholar CrossRef Search ADS Green P. J. ( 1995 ). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82 , 711 – 32 . Google Scholar CrossRef Search ADS Hastie T. J. & Tibshirani R. J. 1990 . Generalized Additive Models . Boca Raton, Florida : Chapman & Hall. Haug O., Dimakos X. K., Vårdal F., J., Aldrin M. & Meze-Hausken E. ( 2011 ). Future building water loss projections posed by climate change. Scand. Actuar. J. 2011 , 1 – 20 . Google Scholar CrossRef Search ADS Heikkinen J. & Arjas E. ( 1998 ). Non-parametric Bayesian estimation of a spatial Poisson intensity. Scand. J. Statist. 25 , 435 – 50 . Google Scholar CrossRef Search ADS Jones D. R., Schonlau M. & Welch W. J. ( 1998 ). Efficient global optimization of expensive black-box functions. J. Global Optimiz. 13 , 455 – 92 . Google Scholar CrossRef Search ADS Knorr-Held L. 2003 . Some remarks on Gaussian Markov random field models for disease mapping. In Highly Structured Stochastic Systems , Green P. J. Hjort N. L. & Richardson S. eds. Oxford : Oxford University Press , pp. 203 – 7 . Lin L. & Dunson D. B. ( 2014 ). Bayesian monotone regression using Gaussian process projection. Biometrika 101 , 303 – 17 . Google Scholar CrossRef Search ADS Luss R., Rosset S. & Shahar M. ( 2012 ). Efficient regularized isotonic regression with application to gene–gene interaction search. Ann. Appl. Statist. 6 , 253 – 83 . Google Scholar CrossRef Search ADS Møller J., Pettitt A. N., Reeves R. & Berthelsen K. K. ( 2006 ). An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika 93 , 451 – 8 . Google Scholar CrossRef Search ADS Penttinen A., Stoyan D. & Henttonen H. M. ( 1992 ). Marked point processes in forest statistics. Forest Sci. 38 , 806 – 24 . Ramsay J. O. 1998 . Estimating smooth monotone functions. J. R. Statist. Soc. B 60 , 365 – 75 . Google Scholar CrossRef Search ADS Ramsay J. O. & Silverman B. W. 2005 . Functional Data Analysis . New York : Springer , 2nd ed . Roustant O., Ginsbourger D. & Deville Y. ( 2012 ). DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J. Statist. Software 51 , 1 – 55 . Google Scholar CrossRef Search ADS Royston P. ( 2000 ). A useful monotonic non-linear model with applications in medicine and epidemiology. Statist. Med. 19 , 2053 – 66 . Google Scholar CrossRef Search ADS Rue H. & Held L. 2005 . Gaussian Markov Random Fields: Theory and Applications . Boca Raton, Florida : Chapman & Hall . Saarela O. & Arjas E. ( 2011 ). A method for Bayesian monotonic multiple regression. Scand. J. Statist. 38 , 499 – 513 . Scheel I., Ferkingstad E., Frigessi A., Haug O., Hinnerichsen M. & Meze-Hausken E. ( 2013 ). A Bayesian hierarchical model with spatial variable selection: The effect of weather on insurance claims. Appl. Statist. 62 , 85 – 100 . Scott J. G., Shively T. S. & Walker S. G. ( 2015 ). Nonparametric Bayesian testing for monotonicity. Biometrika 102 , 617 – 30 . Google Scholar CrossRef Search ADS Shively T. S., Sager T. W. & Walker S. G. 2009 . A Bayesian approach to non-parametric monotone function estimation. J. R. Statist. Soc. B 71 , 159 – 75 . Google Scholar CrossRef Search ADS Tutz G. & Leitenstorfer F. ( 2007 ). Generalized smooth monotonic regression in additive modeling. J. Comp. Graph. Statist. 16 , 165 – 88 . Google Scholar CrossRef Search ADS Wakefield J. ( 2007 ). Disease mapping and spatial regression with count data. Biostatistics 8 , 158 – 83 . Google Scholar CrossRef Search ADS PubMed Walker S. G. & Hjort N. L. 2001 . On Bayesian consistency. J. R. Statist. Soc. B 63 , 811 – 21 . Google Scholar CrossRef Search ADS Waller L. A. & Gotway C. A. 2004 . Applied Spatial Statistics for Public Health Data . Hoboken, New Jersey : Wiley . Wilson A., Reif D. M. & Reich B. J. ( 2014 ). Hierarchical dose–response modeling for high-throughput toxicity screening of environmental chemicals. Biometrics 70 , 237 – 46 . Google Scholar CrossRef Search ADS PubMed Zhang L. & Shi H. ( 2004 ). Local modeling of tree growth by geographically weighted regression. 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# Bayesian spatial monotonic multiple regression

, Volume 105 (3) – Sep 1, 2018
17 pages

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© 2018 Biometrika Trust
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0006-3444
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1464-3510
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10.1093/biomet/asy019
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### Abstract

Summary We consider monotonic, multiple regression for contiguous regions. The regression functions vary regionally and may exhibit spatial structure. We develop Bayesian nonparametric methodology that permits estimation of both continuous and discontinuous functional shapes using marked point process and reversible jump Markov chain Monte Carlo techniques. Spatial dependence is incorporated by a flexible prior distribution which is tuned using crossvalidation and Bayesian optimization. We derive the mean and variance of the prior induced by the marked point process approach. Asymptotic results show consistency of the estimated functions. Posterior realizations enable variable selection, the detection of discontinuities and prediction. In simulations and in an application to a Norwegian insurance dataset, our method shows better performance than existing approaches. 1. Introduction Geospatial data arise in forestry (Penttinen et al., 1992), epidemiology (Waller & Gotway, 2004) and other domains. Due to practicality or confidentiality concerns, locally aggregated data are common and are typically available on an irregular lattice. Statistical methods for such data aim to explore the association between a response and explanatory variables while accounting for spatial dependence in the model parameters. To introduce such dependence, a neighbourhood structure, often based upon the arrangement of the areal units on a map, is typically defined via an adjacency matrix. Most modelling frameworks assume a common effect of the explanatory variables for all regions (Waller & Gotway, 2004; Wakefield, 2007). Spatial variation is then typically accommodated via a spatially structured random effect on the intercept. Some applications, however, need to allow for a spatially varying regression function (Bell et al., 2004; Zhang & Shi, 2004; Cahill & Mulligan, 2007). Statistical methods for such scenarios are available for generalized linear (Fotheringham et al., 2003; Assunção, 2003; Scheel et al., 2013) and additive models (Congdon, 2006). However, these approaches are limited, as continuity of the regression function is assumed: abrupt changes in the regression surface are not captured unless they are explicitly included in the model. Neglecting such effects may result in a bias due to oversmoothing; see Bowman & Azzalini (1997, p. 26). Since continuity may be inappropriate, we replace it by monotonicity (Royston, 2000; Farah et al., 2013; Wilson et al., 2014). Whilst continuity cannot, in general, be verified, tests of monotonicity are available (Bowman et al., 1998; Ghosal et al., 2000; Scott et al., 2015). Based upon a number of observations for each region, we develop Bayesian nonparametric methodology which estimates the regional regression functions whilst exploiting any neighbourhood structure. The estimation of a single monotonic function is usually called isotonic regression. Early publications discussed inference under monotonicity constraints (Ayer et al., 1955; Brunk, 1955; Barlow & Brunk, 1972), and solution algorithms are available (Brunk et al., 1957; Luss et al., 2012). Isotonic regression is further considered for additive (Bacchetti, 1989; Tutz & Leitenstorfer, 2007) and high-dimensional models (Fang & Meinshausen, 2012; Bergersen et al., 2014), in functional data analysis (Ramsay, 1998; Ramsay & Silverman, 2005) and in Bayesian nonparametric modelling (Gelfand & Kuo, 1991; Shively et al., 2009; Saarela & Arjas, 2011; Lin & Dunson, 2014). In order to learn about potentially spatially structured monotonic regression functions, a dependence model for functions, possibly with discontinuities, is required. Our approach represents each monotonic regional function by a set of marked point processes. Potential spatial structure is modelled via a joint prior distribution, which is based upon a flexible discrepancy measure. The prior allows the functional dependence to be constant, increasing or decreasing with increasing function values. The Bayesian framework induces a consistent posterior (Barron et al., 1999; Walker & Hjort, 2001), and permits both smooth contours and discontinuities. To tune the prior, we combine crossvalidation and Bayesian optimization. Realizations of the posterior are obtained by a reversible jump Markov chain Monte Carlo algorithm (Green 1995) and enable variable selection, prediction and the detection of discontinuities. 2. Modelling and inference 2.1. Likelihood and notation Consider $$K$$ contiguous regions whose neighbourhood structure is given by an adjacency matrix or a lattice graph. Let $$y_k\in\mathbb{R}$$ and $$x_k\in\mathbb{R}^m$$ denote the response and explanatory variables, respectively, for region $$k$$ ($$k=1,\ldots,K$$). The likelihood is $$f\left\{\,y_k\mid\lambda_k(x_k), \theta_k\right\}\!,$$ (1) where $$\lambda_k:\mathbb{R}^m\to\left[\delta_{\min}, \delta_{\max}\right]$$ is the monotonic regression function for region $$k$$, for which $$\lambda_k\left(x_k\right)$$ is assumed to lie within the predefined interval $$\left[\delta_{\min}, \delta_{\max}\right]$$. Monotonicity is defined in terms of the partial Euclidean ordering $$\preceq$$: if $$u\leqslant v$$ componentwise, then $$\lambda_k(u)\leqslant \lambda_k(v)$$, for $$u,v\in \mathbb{R}^m$$. The vector $$\theta_k$$ denotes additional, potentially spatially varying, model parameters which are a priori independent of $$\lambda_1,\ldots,\lambda_K$$. In what follows, we perform inference on $$\lambda_1$$ through $$\lambda_K$$ while accounting for potential spatial structure in these functions. Each $$\lambda_k$$ ($$k=1,\ldots,K$$) is estimated over a closed set $$X\subset\mathbb{R}^m$$. In applications, $$X$$ and $$[\delta_{\min},\delta_{\max}]$$ may be defined in terms of the explanatory variables and responses, respectively. For instance, if $$\lambda_k\left(x_k\right)$$ in (1) is the mean response, $$\delta_{\min}$$ may be set to the minimum observed response across the $$K$$ regions. In § 2.2–2.4 we complete the Bayesian framework by defining a joint prior on $$\lambda_1,\ldots,\lambda_K$$, while § 2.5 and 2.6 detail the estimation procedure. 2.2. A spatial dependence model for the monotonic functions We wish to impose spatial structure on $$\lambda_1,\ldots,\lambda_K$$ and hence define a joint prior density that favours these functions being similar. We set $$\delta_{\min}=0$$ and write $$\lambda_k(x)$$ instead of $$\lambda_k(x)-\delta_{\min}$$ ($$k=1,\ldots,K$$) below. First, we introduce a pairwise discrepancy measure for $$\lambda_k$$ and $$\lambda_{k'}$$ ($$k,k'=1,\ldots,K; k\neq k'$$). Such a measure should be minimal if and only if $$\lambda_k$$ and $$\lambda_{k'}$$ are equal, and should increase with an increasing difference in these functions. A possible choice is the integrated squared distance $$\int_{X} \left\{\lambda_k(x) - \lambda_{k'}(x)\right\}^2\,\mbox{d}x\text{.}$$ (2) Sometimes we have prior knowledge that differences in the lower, or higher, function values of $$\lambda_k$$ and $$\lambda_{k'}$$ should be downweighted, or avoided. For example, increased measurement error in higher values of the explanatory variables may be better handled through increased information borrowing. Thus, we replace $$\left\{\lambda_k(x) - \lambda_{k'}(x)\right\}^2$$ in (2) by $$\gamma_{p}\left\{\lambda_k(x),\lambda_{k'}(x)\right\}=\left[\left\{\lambda_k(x)\right\}^p-\left\{\lambda_{k'}(x)\right\}^p\right]\left\{\lambda_k(x)-\lambda_{k'}(x)\right\}\!,\qquad p>0,$$ (3) for which $$p=1$$ yields the integrated squared distance. See the 2017 Lancaster University PhD thesis by C. Rohrbeck for a more general formulation. Expression (3) can also be interpreted as the squared distance with weight $$\left[\left\{\lambda_k(x)\right\}^p-\left\{\lambda_{k'}(x)\right\}^p\right]/ \left\{\lambda_k(x)-\lambda_{k'}(x)\right\}$$. Figure 1 illustrates the behaviour of $$\gamma_{p}\left\{\lambda_k(x),\lambda_{k'}(x)\right\}$$ at a fixed point $$x\in\mathbb{R}^m$$ for different settings of $$p$$. For brevity, let $$\kappa=\lambda_k(x)$$ and $$\psi=\lambda_{k'}(x)$$. Figure 1(a) shows that $$\gamma_p\{\kappa,\psi\}$$ increases with an increasing difference between $$\kappa$$ and $$\psi=0$$ for all settings of $$p$$. Hence, $$\gamma_p$$ satisfies the desired properties stated above. Furthermore in Fig. 1(b), the fixed difference $$\psi = \kappa + 1$$ is penalized more for higher $$\kappa$$ if $$p>1$$, while being penalized less for $$p<1$$. A constant penalty is induced for $$p=1$$. As such, the parameter $$p$$ allows the penalty for differences between $$\lambda_k$$ and $$\lambda_{k'}$$ to vary with the function values. Fig. 1. View largeDownload slide Behaviour of $$\gamma_{p}\left\{\kappa,\psi\right\}$$ with respect to $$\kappa$$ for $$p=1$$ (solid), $$p=2$$ (dashes), $$p=0{\cdot}5$$ (dots) and $$p=0{\cdot}2$$ (dot-dash), subject to (a) $$\psi =0$$ and (b) $$\psi = \kappa + 1$$ being fixed. Fig. 1. View largeDownload slide Behaviour of $$\gamma_{p}\left\{\kappa,\psi\right\}$$ with respect to $$\kappa$$ for $$p=1$$ (solid), $$p=2$$ (dashes), $$p=0{\cdot}5$$ (dots) and $$p=0{\cdot}2$$ (dot-dash), subject to (a) $$\psi =0$$ and (b) $$\psi = \kappa + 1$$ being fixed. The dependence model for the $$K$$-set $$\lambda_1,\ldots,\lambda_K$$ is then defined as a Gibbs measure (Georgii, 2011) with the discrepancy measure constructed in (2) and (3) as a pair-potential. Formally, $$\pi\left(\lambda_1, \ldots, \lambda_K\mid\omega\right) \propto \prod_{1\leqslant k<k'\leqslant K} \exp \left[-\omega\ d_{k,k'}\int_{X}\gamma_{p}\left\{\lambda_k(x),\lambda_{k'}(x)\right\}\mbox{d}x\right]\!,\qquad\omega\geqslant0,$$ (4) where the product is over all pairs of regions. The constant $$d_{k,k'}\geqslant0$$ describes our prior belief concerning the degree of similarity of $$\lambda_k$$ and $$\lambda_{k'}$$. In spatial statistics, we often set $$d_{k,k'}=1$$ if the regions $$k$$ and $$k'$$ are adjacent and $$d_{k,k'}=0$$ otherwise. Such a choice reduces the computational cost since the integral in (4) need only be evaluated for pairs of adjacent regions. The degree of dependence increases in $$\omega$$, and $$\omega=0$$ corresponds to $$\lambda_1,\ldots,\lambda_K$$ being independent. Sensitivity to choice of $$p$$ is explored in § 3.3. Expression (4) can be extended to regionally varying $$X$$, permitting borrowing of information for extrapolation; see the Supplementary Material. 2.3. Marked point process prior We specify an individual prior model for $$\lambda_k:X\to\left[\delta_{\min},\delta_{\max}\right]$$ ($$k=1,\ldots,K$$) and drop the index $$k$$ in the rest of this subsection for brevity. Prior distributions proposed in the literature include an ordered Dirichlet process (Gelfand & Kuo, 1991) and a constrained spline (Shively et al., 2009). Our prior is similar to that of Saarela & Arjas (2011): $$\lambda$$ is postulated to be a nondecreasing step function with $$\lambda(x)\in\left[\delta_{\min},\delta_{\max}\right]$$; any monotonic, bounded function can be approximated to a desired accuracy by increasing the number of steps. The location and height of the steps of $$\lambda$$ define a marked point process on $$X$$. Following Saarela & Arjas (2011), we represent $$\lambda$$ via a set of $$I$$ marked point processes, $$\Delta = \left(\Delta_1,\ldots,\Delta_I\right)$$, where $$\Delta_i$$ ($$i=1,\ldots,I$$) is on a set $$X_i$$ with $$\bigcup_{i=1}^I X_i=X$$. Here, we define $$X_1,\ldots,X_I$$ based on the nonempty subsets of $$\left\{1,\ldots,m\right\}$$. For example, if $$m=2$$ we choose $$I=3$$ and have separate processes $$\Delta_1$$ and $$\Delta_2$$ for each of the two explanatory variables, $$x_1$$ and $$x_2$$, respectively, and one process $$\Delta_3$$ for both components, $$\left(x_1,x_2\right)$$, jointly. Figure 2 provides an example for $$X=[0,1]^2$$. The benefits of this representation are discussed later in this subsection. Fig. 2. View largeDownload slide Point locations to represent a step function $$\lambda$$ on $$X=[0,1]^2$$ via a set of $$I=3$$ marked point processes $$(\Delta_1,\Delta_2,\Delta_3)$$. The processes $$\Delta_1$$ (triangle), $$\Delta_2$$ (square) and $$\Delta_3$$ (diamond) are defined on the sets $$X_1=[0,1]\times 0$$, $$X_2=0\times[0,1]$$ and $$X_3=(0,1]\times(0,1]$$, respectively. Fig. 2. View largeDownload slide Point locations to represent a step function $$\lambda$$ on $$X=[0,1]^2$$ via a set of $$I=3$$ marked point processes $$(\Delta_1,\Delta_2,\Delta_3)$$. The processes $$\Delta_1$$ (triangle), $$\Delta_2$$ (square) and $$\Delta_3$$ (diamond) are defined on the sets $$X_1=[0,1]\times 0$$, $$X_2=0\times[0,1]$$ and $$X_3=(0,1]\times(0,1]$$, respectively. We now formalize the representation of $$\lambda$$ via $$\Delta$$ and let \begin{equation*} \Delta_{i} = \left\{\left(\xi_{i,j}, \delta_{i,j}\right)\in X_i\times\left[\delta_{\min},\delta_{\max}\right] {\ :\ \ } j = 1,\ldots,n_{i}\right\}\qquad (i=1,\ldots,I)\text{.} \end{equation*} Here, $$\xi_{i,j}$$ and $$\delta_{i,j}$$ refer to a point location and associated mark, respectively, and $$n_i$$ is the number of points in $$\Delta_i$$. Monotonicity is imposed by constraining the marks: if $$\xi_{i,j} \preceq \xi_{i',j'}$$, then $$\delta_{i,j} \leqslant \delta_{i',j'}$$ ($$i,i'=1,\ldots,I$$; $$j=1,\ldots,n_i$$; $$j'=1,\ldots,n_{i'})$$. The value $$\lambda(x)$$ is then defined as the largest mark $$\delta_{i,j}$$ such that $$x$$ imposes a monotonicity constraint on the associated point location $$\xi_{i,j}$$. Formally, $$\lambda(x) = \max_{i,j} \left\{\delta_{i,j} {\ :\ } \xi_{i,j}\preceq x\right\}\!\text{.}$$ (5) Representing $$\lambda$$ via the set $$(\Delta_1,\ldots,\Delta_I)$$ facilitates variable selection. Let $$X=[0,1]^m$$ and suppose that the explanatory variable $$x_1$$ is redundant. Hence, $$\lambda$$ is constant with increasing values of $$x_1$$, that is, $$\lambda(x) = \lambda\left\{x + (\epsilon, 0,\ldots,0)\right\}$$ ($$x\in X; \epsilon>0$$). As we represent $$\lambda$$ via a marked point process, the redundancy of $$x_1$$ implies that the point locations are in the set $$0 \times [0,1]^{m-1}$$. For instance, if $$m=2$$, all points then lie on the line $$x_1=0$$ in Fig. 2. As such, the processes $$\Delta_1$$ and $$\Delta_3$$ contain no points. Consequently, $$n_i$$ ($$i=1,\ldots,I$$) provides an indicator of the redundancy of explanatory variables. The association defined in (5) results in a mapping between the spaces of step functions and marked point processes with monotonicity constraints. We define a prior for $$\lambda$$ via one for $$\Delta$$. The prior on $$N\,{=}\,\sum_{i=1}^{I} n_i$$, the number of steps representing $$\lambda$$, is chosen to be geometric; with $$P(N\,{=}\,n)\,{=}\,(1-\eta)\eta^n$$ for $$n\,{=}\,0,1,\ldots$$, for some specified hyperparameter $$\eta\,{>}\,1$$. This choice allows the possibility that $$N\,{=}\,0$$, which corresponds to $$\lambda\,{=}\,\delta_{min}$$ being constant. This choice promotes model parsimony and favours $$\lambda$$ having few steps. Given $$N$$, the vector $$\left(n_1,\ldots,n_I\right)$$ is uniformly distributed over the set of possibilities of allocating $$N$$ points to the $$I$$ processes. For $$\Delta_i$$ ($$i=1,\ldots,I$$), the location $$\xi_{i,j}$$ ($$\,j\,{=}\,1,\ldots,n_i$$) is uniformly distributed on $$X_i$$. The marks $$\left\{\delta_{i,j}: j=1,\ldots,n_i; i=1,\ldots,I\right\}$$ are uniformly distributed on $$\left[\delta_{\min},\delta_{\max}\right]$$, subject to the monotonicity constraints imposed by the locations in $$\Delta_1,\ldots,\Delta_I$$. Using this hierarchical structure, we obtain the prior density $$\phi\left(\Delta\mid \eta\right) = \pi\left(\left\{\delta_{i,j}\right\} \mid\left\{\xi_{i,j}\right\}\right) \left\{\prod_{i=1}^I\prod_{j=1}^{n_i} \pi\left(\xi_{i,j}\right)\right\} \pi\left(n_1,\ldots,n_I\mid N\right)\pi\left(N\mid\eta\right)\!;$$ (6) further details are provided in the Supplementary Material. The density $$\phi\left(\Delta\mid \eta\right)$$ induces a density on the space of step functions, $$\tilde{\phi}\left(\lambda\mid\eta\right)$$, which can be characterized as follows. Proposition 1. Let $$X=[0,1], \delta_{\min}=0$$ and $$\delta_{\max}=1$$. Then the distribution with density $$\tilde{\phi}\left(\lambda\mid\eta\right)$$ has \begin{align*} E\left\{\lambda(x)\mid\eta\right\} &= x \sum_{n=1}^\infty \left\{\frac{1}{\eta}\left(1-\frac{1}{\eta}\right)^n \frac{n}{n+1}\right\} = x\left(1-\frac{\log\eta}{\eta-1}\right)\!,\\ {\rm var}\left\{\lambda(x)\mid\eta\right\} &=\sum_{n=1}^\infty \left\{\frac{1}{\eta}\left(1-\frac{1}{\eta}\right)^n \frac{nx(2- x + nx)}{(n+1)(n+2)}\right\} - E\left\{\lambda(x)\mid\eta\right\}^2\!\text{.} \end{align*} Hence, the expectation is a linear function whose slope depends on $$\eta$$. See the Supplementary Material for the the proof of Proposition 1. This Bayesian framework has one small limitation. If, for instance, $$X=[0,1]$$, then $$\lambda(0)=\delta_{\min}$$ almost surely. To address this, we define $$\lambda\left(x\right)=\mu + \varphi\left(x\right)$$, where $$\varphi: X\to [\delta_{\min},\delta_{\max}]$$ is monotonic and $$\mu\in\mathbb{R}$$, and with priors $$\tilde{\phi}(\varphi\mid\eta)$$ and $$\pi(\mu)$$, respectively. A second approach is presented in the Supplementary Material. 2.4. Combining the spatial dependence model and marked point process prior We now impose a spatial structure on the $$K$$ sets of marked point processes $$\Delta_1,\ldots,\Delta_K$$, $$\Delta_k = \left(\Delta_{k,1},\ldots,\Delta_{k,I}\right)$$ ($$k=1,\ldots,K$$), by combining $$\phi\left(\Delta_k\mid\eta\right)$$ in (6) with $$\pi\left(\lambda_1,\ldots,\lambda_K\mid\omega\right)$$ in (4). The joint prior $$\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega, \eta\right)$$ is then proportional to $$\prod_{1\leqslant k<k'\leqslant K} \exp\left[-\omega\,d_{k,k'}\,\int_{X}\gamma_{p}\left\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\right\}\,\mbox{d}x\right] \times \prod_{k=1}^K \phi\left(\Delta_k\mid \eta\right)\!,$$ (7) where $$\tilde\lambda_k$$ and $$\tilde\lambda_{k'}$$ are the step functions represented by $$\Delta_k$$ and $$\Delta_{k'}$$, respectively. Since $$\tilde\lambda_1,\ldots,\tilde\lambda_K$$ are step functions, the integral in (4) simplifies to a sum and can be computed efficiently. The full conditional prior density for $$\Delta_k$$ in (7) converges to (6) as $$\omega\to0$$. Further, $$\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega, \eta\right)$$ is proper because $$\pi(\tilde\lambda_1,\ldots,\tilde\lambda_K\mid\omega)$$ lies within $$(0,1]$$ and $$\phi(\Delta_k\mid\eta)$$ is a proper density function. The likelihood (1) and prior (7) specify a posterior distribution for $$\Delta_1,\ldots,\Delta_K$$ with density $$\pi\left(\Delta_1, \ldots,\Delta_K \mid \mathcal{D}, \omega,\eta\right)\propto \left[\prod_{k=1}^K \prod_{t=1}^{T_k} f\left\{\,y_{k,t}\mid\tilde\lambda_k\left(x_{k,t}\right)\!,\theta_k\right\}\right] \times\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega,\eta\right)\!,$$ (8) where $$\mathcal{D}$$ denotes the data and $$T_k$$ is the number of observations for region $$k$$ ($$k=1,\ldots,K$$). An estimator should be consistent. In Bayesian nonparametrics, consistency is often considered in terms of the Hellinger distance. Let $$\left(\lambda_k,\theta_k\right)$$ denote the true model parameters for region $$k$$ ($$k=1,\ldots,K$$) and let $$G_k$$ be the distribution of the explanatory variables, $$x_k\sim G_k$$. Following Walker & Hjort (2001), we denote the Hellinger distance between the densities with parameters $$(\lambda_k, \theta_k)$$ and $$(\tilde{\lambda}_k,\tilde{\theta}_k)$$ by $$H_k\left(\tilde\lambda_k,\tilde\theta_k\right) = \left(1 - \int \int \left[f\left\{\,y\mid\tilde\lambda_k(x_k),\tilde\theta_k\right\} f\left\{\,y\mid\lambda_k(x_k),\theta_k\right\}\right]^{1/2} \mbox{d}y G_k(\mbox{d}x_k)\right)^{1/2}\!\text{.}$$ (9) Let $$\Lambda = \left(\lambda_1,\ldots,\lambda_K\right)$$ and $$\Theta = \left(\theta_1,\ldots,\theta_K\right)$$. We then define a neighbourhood $$U_{\epsilon}\left(\Lambda, \Theta\right)$$ around the truth $$\left(\Lambda, \Theta\right)$$ with respect to $$H_1,\ldots,H_K$$ in (9) by $U_{\epsilon}\left(\Lambda, \Theta\right) = \left\{\left(\tilde{\Lambda},\tilde{\Theta}\right) {\ :\ } H_k\left(\tilde\lambda_k,\tilde{\theta}_k\right) \leqslant \epsilon, k=1,\ldots,K\right\}\!,\qquad\epsilon>0\text{.}$ Here, $$U_{\epsilon}\left(\Lambda, \Theta\right)$$ contains only step functions and is nonempty because we can approximate $$\lambda_k$$ by a step function to any degree of accuracy. In the following, we focus on $$f\left\{\,y_k\mid \lambda_k\left(x_k\right),\theta_k\right\}$$ being the normal density function with mean $$\lambda_k\left(x_k\right)$$ and variance $$\theta_k$$, but the theory can be generalized and holds for all examples in this paper. Theorem 1. Let $$G_k$$ ($$k=1,\ldots,K$$) be absolutely continuous and assign positive mass to any nondegenerate subset of $$X$$. Further, let the prior $$\pi(\tilde\Theta)$$ put positive mass on any neighbourhood of $$\Theta$$. Then, for $$\lambda_1,\ldots,\lambda_K:X \to \left[\delta_{\min},\delta_{\max}\right]$$ monotonic and continuous and for $$\epsilon>0$$, $$\tilde\Pi\left\{U_{\epsilon}^c\left(\Lambda,\Theta\right)\mid\mathcal{D}, \omega, \eta \right\} \to 0$$ almost surely as $$\min_{k=1,\ldots,K} T_k \to \infty$$. Here, $$U_{\epsilon}^c\left(\Lambda,\Theta\right)$$ is the complement of $$U_{\epsilon}\left(\Lambda,\Theta\right)$$ and $$\tilde\Pi$$ denotes the posterior distribution induced by the likelihood (1) and the priors $$\pi(\tilde\Theta)$$ and $$\pi\left(\Delta_1, \ldots, \Delta_K\mid\omega, \eta\right)$$. Hence, the posterior distribution concentrates around the $$K$$ true functions as the number of data points becomes large, conditional on appropriate boundaries $$\delta_{\min}$$ and $$\delta_{\max}$$. Moreover, the posterior mean may be smooth, as the model permits variability in the number, locations and heights of the steps. Consequently, our approach can recover both smooth and discontinuous functional shapes. This result is well known for the estimation of a single probability density function using a piecewise approximation (Heikkinen & Arjas, 1998). The proof of Theorem 1 is in the Supplementary Material. In a fully Bayesian framework, we would need priors for $$\eta$$ and $$\omega$$. However, the normalizing constant of $$\pi(\Delta_1,\ldots,\Delta_K\mid\omega,\eta)$$ in (7) is intractable, unless $$\omega=0$$. This leads to our novel inferential approach for $$\omega$$ in § 2.6. In terms of setting $$\eta$$, Proposition 1 implies that higher values of $$\eta$$ will generally lead to smoother surfaces. Alternatively, one may learn about $$\eta$$ by considering the case $$\omega=0$$. We can then specify a conjugate beta prior for $$1/\eta$$ and sample from the full conditional beta posterior; the performance of this approach is explored in § 3. 2.5. Inference and analysis of the marked point processes Our scheme to sample from the posterior density in (8) is based on Saarela & Arjas (2011). Initially, $$\Delta_{k,i}$$ ($$k=1,\ldots,K; i=1,\ldots,I$$) is empty and so $$\lambda_k=\delta_{\min}$$. The $$K$$ sets are then updated sequentially. We first select one of the processes $$\Delta_{k,1},\ldots,\Delta_{k,I}$$ ($$k=1,\ldots,K$$) with equal probability. For the sampled process $$\Delta_{k,i^*}$$, we randomly propose one of three moves, implying local changes of $$\lambda_k$$. A birth move adds a point $$\left(\xi^*,\delta^*\right)$$ to $$\Delta_{k,i^*}$$, where $$\xi^*$$ is sampled uniformly on $$X_{i^*}$$. Given $$\xi^*$$, the associated mark $$\delta^*$$ is sampled uniformly, subject to monotonicity being preserved. A death move removes a point from $$\Delta_{k,i^*}$$, maintaining reversibility. A shift move changes the location and mark of a point in $$\Delta_{k,i^*}$$, subject to the partial order imposed by the monotonicity constraints being maintained. See the Appendix for details and the acceptance probabilities. We implemented this scheme in C++, and a simulation study to verify correctness is provided in the Supplementary Material. Realizations sampled from the posterior distribution are rich and facilitate detailed analysis of $$\lambda_1,\ldots,\lambda_K$$. Thinning of the Markov chains is needed to reduce autocorrelation. Posterior mean estimates for $$\lambda_k$$ are obtained by averaging over the stored realizations. The mean and quantiles of the posterior distribution are accessible for any $$x\in X$$ by deriving $$\lambda_k(x)$$ for each sample. Further, the samples facilitate the detection of discontinuities; see the Supplementary Material. 2.6. Estimation of $$\omega$$ The performance of our approach relies on a suitable $$\omega$$ in (7). If $$\omega$$ is too high, spatial variation is oversmoothed, while overfitting may occur if $$\omega$$ is too small. Since the normalizing constant of (7) is intractable, we cannot sample from the full conditional distribution of $$\omega$$ via an additional Gibbs step within the scheme in § 2.5. Further, while there exists a rich literature on handling intractable normalizing constants (Beaumont et al., 2002; Møller et al., 2006; Andrieu & Roberts, 2009), these approaches cannot be adapted for use here since efficient sampling from the prior distribution in (7) is infeasible. Hence, we estimate $$\omega$$ prior to inference on $$\Delta_1,\ldots,\Delta_K$$. One approach is $$s$$-fold crossvalidation: the data for each of the $$K$$ regions are split into $$s$$ subsets of equal size. The sampling scheme in § 2.5 is then performed $$s$$ times with varying training and test data. Parameter values are compared by the posterior mean squared error for the test data points. In order to keep the number of evaluated values for $$\omega$$ small, we combine crossvalidation with the global optimization algorithm of Jones et al. (1998). Efficient global optimization postulates a sequential design strategy to detect global extrema of a black-box function $$r$$. The algorithm is widely applied in simulations if $$r$$ is costly to evaluate and the parameter space $$Z$$ is small (Roustant et al., 2012). The rationale is to model $$r$$ by a Gaussian process $$R$$ which is updated sequentially. Specifically, the proposal $$z^*\in Z$$ is selected to maximize the expected improvement $$E\left[\max\left\{r_{\rm opt}-R(z),0\right\}\right],\qquad z\in Z,$$ (10) where $$r_{\rm opt}$$ denotes the current optimum. Hence, (10) represents the potential of $$r(z)$$ to be smaller than $$r_{\rm opt}$$. The proposal is evaluated until its expected improvement falls below a critical value, corresponding to $$r_{\rm opt}$$ being sufficiently close to the unknown minimum of $$r$$. As this approach balances local exploration of the areas likely to provide good model fit and a global search, a suitable solution is generally found after a reasonable number of evaluations. When estimating $$\omega$$, interest lies in the minimum of the crossvalidation function CV($$\omega$$). Algorithm 1 sketches our approach. We first derive an upper bound as efficient global optimization can only be applied to a closed set. An initial bound $$\omega_u$$ is increased until its mean squared error is greater than that for $$\omega=0$$ by a sufficient amount; $$\beta=2$$ in Algorithm 1 proved reasonable in our simulations. Once $$\omega_u$$ is fixed, an initial proposal $$\omega^*\in\left[0, \omega_u\right]$$ is made, guaranteeing that $$R$$ in (10) is fitted with at least three data points. We use the DiceOptim R package (Roustant et al., 2012) to derive the expected improvement and run multiple $$s$$-fold crossvalidations with the same $$\omega$$ to reduce the dependence on the split of the data. The mean and variance of the mean squared error across the repetitions are used to fit $$G$$. We then repeatedly perform crossvalidation and update $$\omega^*$$ until the maximum expected improvement falls below the critical value $$\alpha$$. To conclude, we set $$\omega$$ to the value $$\omega_{\rm opt}$$ that provided the lowest mean squared error. Algorithm 1. Combination of efficient global optimization and crossvalidation. Set initial upper bound $$\omega_u$$, critical value $$\alpha$$ and factor $$\beta$$ Perform crossvalidation for $$\omega=0$$ and store CV$$(0)$$ While CV$$(\omega_u) {\ <\ } \beta$$CV$$(0)$$ Increase $$\omega_u$$ Perform crossvalidation for $$\omega_u$$ and store CV$$(\omega_u)$$ Set initial proposal $$\omega^*$$, e.g. $$\omega^* = \omega_u/2$$ Initialize maximum expected improvement $$M>\alpha$$ While $$M>\alpha$$ Perform crossvalidation for $$\omega^*$$ and store CV$$(\omega^*)$$ Fit Gaussian process $$R$$ Update $$\omega^*$$ and $$M$$ Return value $$\omega_{\rm opt}$$ which provided smallest error 3. Simulation study 3.1. Introduction We aim to demonstrate that our method improves estimates if similarities between functions exist, and is robust otherwise. Furthermore, we examine sensitivity to the prior parameters $$p$$ and $$\eta$$ in expression (7). Responses for region $$k$$ ($$k=1,\ldots,K$$) are simulated independently from \begin{equation*} y_k\mid x_k\sim {N}\left\{\lambda_k(x_k), \theta_k\right\}\!, \end{equation*} where $$x_k\in[0,1]^2$$. As described in § 2.3, we define $$\lambda_k(x) = \mu_k + \varphi_k(x)$$ and perform inference on $$\mu_k\in\mathbb{R}$$ and $$\varphi_k:[0,1]^2\to\left[\delta_{\min},\delta_{\max}\right]$$. The likelihood (1) is then \begin{equation*} f\left\{\,y_k \mid \varphi_k\left(x_k\right)\!, \mu_k, \theta_k\right\} = \left(\frac{1}{2\pi\theta_k}\right)^{1/2}\exp\left[-\frac{1}{2\theta_k}\left\{\,y_k-\mu_k-\varphi_k(x_k)\right\}^2\right]\!\text{.} \end{equation*} An intrinsic conditional autoregressive prior (Besag et al., 1991; Rue & Held, 2005) is defined for $$(\mu_1,\ldots,\mu_K)$$ and imposes a spatial structure. Here, $$\mu_1,\ldots,\mu_K$$ are updated separately via a random walk Metropolis step and the hyperparameter in $$\pi\left(\mu_1,\ldots,\mu_K\right)$$ is updated via Gibbs sampling (Knorr-Held, 2003). Furthermore, we assign the prior distribution $$1/\theta_k \sim \mbox{Ga}\left(1, 0{\cdot}001\right)$$ ($$k=1,\ldots,K$$) and update $$\theta_1,\ldots,\theta_K$$ via Gibbs sampling. Here, $$X$$ is the square spanned by the minimum and maximum observed values in each explanatory variable across the $$K$$ regions. The boundaries are set to $$\delta_{\min} = -1$$ and $$\delta_{\max}=4$$. We assess performance via the absolute difference of the posterior mean estimate $$\hat{\lambda}_k$$ and the true function $$\lambda_k$$, over a regular $$100\times 100$$ grid on $$X$$. Only grid points contained in the convex hull of the observed values of $$x_k$$ ($$k=1,\ldots,K$$) are considered. Improvements are discussed with respect to the setting $$\omega=0$$, which imposes no dependence. Algorithm 1 is applied with $$\beta=2, \alpha=\,$$$${\small{\text{CV}}}(0)/1000$$ and $$\omega_u=50$$. We increase $$\omega_u$$ by a factor of 10 until CV($$\omega_u$$) $$<$$ 2 CV(0). For each proposed $$\omega$$, five repetitions of 10-fold crossvalidation are performed. A fold consists of 50 000 iterations and every 100th sample is stored after a burn-in period of 25 000 iterations. In addition to the expected improvement criterion, we stop if 30 proposals have been considered; this occurred once in all our simulations. Birth, death and shift moves are proposed with probabilities 0$${\cdot}$$3, 0$${\cdot}$$3 and 0$${\cdot}$$4. Estimates for $$\Delta_1,\ldots,\Delta_K$$ are based on 3 000 000 iterations, with the first 1 000 000 discarded and then every 1000th sample stored. Convergence of the sampled Markov chains for $$\Delta_k$$ ($$k=1,\ldots,K$$) is checked via the trace plots of $$\lambda_k(x)$$ for ten random points in $$X$$. Posterior mean plots and trace plot examples are provided in the Supplementary Material. We also applied our method to non-Gaussian settings; an example with binomial response data is presented in the Supplementary Material. The C++ and R code for all simulations is provided in the Supplementary Material. 3.2. Sensitivity analysis on $$\eta$$ We explore general performance and sensitivity to $$\eta$$ based on five simulations with $$K=2$$ regions. Columns 1 and 2 in Fig. 3 illustrate the five pairs of $$(\lambda_1,\lambda_2)$$. Across all studies, $$\lambda_k\left(x_k\right)\in\left[0,2\right]$$ ($$k=1,2$$). For each study, 1000 and 100 data points are sampled for regions 1 and 2, respectively, with $$\theta_k=0{\cdot}05^2$$ and $$x_k\sim\mbox{Un}([0,1]^2)$$ ($$k=1,2$$). This setting explores the potential benefits of borrowing statistical information from region 1 when estimating $$\lambda_2$$. We fix the prior parameter $$p=1$$ and consider three settings for $$\eta$$: (i) $$\eta=10$$, (ii) $$\eta=1000$$ and (iii) $$\eta=\hat{\eta}$$. Here, $$\hat{\eta}$$ is the posterior mean estimate for $$\eta$$ in the case $$\omega=0$$ as described in § 2.4. Fig. 3. View largeDownload slide True functions $$\lambda_1$$ (column 1) and $$\lambda_2$$ (column 2) and the posterior mean estimate $$\hat\lambda_2$$ obtained for $$\eta= 1000$$(Column 3) for the simulations in § $$3.2$$. Fig. 3. View largeDownload slide True functions $$\lambda_1$$ (column 1) and $$\lambda_2$$ (column 2) and the posterior mean estimate $$\hat\lambda_2$$ obtained for $$\eta= 1000$$(Column 3) for the simulations in § $$3.2$$. We also estimate a monotonized generalized additive model for each region separately and derive the same summary statistics as for our approach. We first fit a generalized additive model (Hastie & Tibshirani, 1990) and then apply the projection by Lin & Dunson (2014); plots of the estimated surfaces are provided in the Supplementary Material. Studies 1 and 2 consider the case $$\lambda_1 = \lambda_2$$ and Table 1 shows reduced error measures, particularly for region 2, compared to the setting where $$\omega=0$$. Figure 3 illustrates that both smooth surfaces and discontinuities are recovered well. In Study 3 and Study 4, $$\lambda_1$$ and $$\lambda_2$$ are similar and the conclusions are consistent with those for Study 1 and Study 2. Study 5 considers the case of $$\lambda_1$$ being smooth while $$\lambda_2$$ is piecewise linear. Table 1 shows no worsening in the error measures, demonstrating robustness of our method. The prospect of variable selection described in § 2.3 has been examined for $$\lambda_2$$ in Study 5, where $$\lambda_2(x)$$ depends only on $$x_{2,1}$$. Almost all sampled points are in $$\Delta_{2,1}$$; hence the results indicate $$x_{2,2}$$ to be redundant. Table 1. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2)$$ in Studies 1 to 5 in Fig. 3 for $$\eta=(10,1000,\hat{\eta})$$, $$\omega=0$$ and GAM, an estimated monotonized generalized additive model Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Table 1. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2)$$ in Studies 1 to 5 in Fig. 3 for $$\eta=(10,1000,\hat{\eta})$$, $$\omega=0$$ and GAM, an estimated monotonized generalized additive model Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Study Function $$\eta=10$$ $$\eta=1000$$ $$\eta=\hat{\eta}$$ $$\omega=0$$ GAM 1 $$\lambda_1$$ 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$0) 1$$\cdot$$8 (2$$\cdot$$1) 1$$\cdot$$8 (2$$\cdot$$1) 3$$\cdot$$2 (3$$\cdot$$4) $$\lambda_2$$ 2$$\cdot$$5 (2$$\cdot$$7) 3$$\cdot$$0 (3$$\cdot$$3) 2$$\cdot$$8 (3$$\cdot$$1) 4$$\cdot$$6 (5$$\cdot$$6) 4$$\cdot$$6 (4$$\cdot$$9) 2 $$\lambda_1$$ 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$2) 1$$\cdot$$6 (3$$\cdot$$3) 7$$\cdot$$0 (7$$\cdot$$0) $$\lambda_2$$ 2$$\cdot$$8 (3$$\cdot$$3) 3$$\cdot$$1 (3$$\cdot$$7) 3$$\cdot$$1 (3$$\cdot$$9) 4$$\cdot$$6 (5$$\cdot$$6) 9$$\cdot$$0 (9$$\cdot$$0) 3 $$\lambda_1$$ 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$1) 1$$\cdot$$1 (1$$\cdot$$0) 0$$\cdot$$5 (0$$\cdot$$5) $$\lambda_2$$ 2$$\cdot$$0 (1$$\cdot$$6) 1$$\cdot$$9 (1$$\cdot$$5) 1$$\cdot$$9 (1$$\cdot$$4) 2$$\cdot$$3 (2$$\cdot$$2) 1$$\cdot$$2 (0$$\cdot$$9) 4 $$\lambda_1$$ 3$$\cdot$$1 (9$$\cdot$$2) 2$$\cdot$$7 (7$$\cdot$$4) 2$$\cdot$$8 (7$$\cdot$$8) 2$$\cdot$$8 (7$$\cdot$$5) 6$$\cdot$$7 (8$$\cdot$$6) $$\lambda_2$$ 4$$\cdot$$2 (9$$\cdot$$2) 4$$\cdot$$0 (7$$\cdot$$8) 4$$\cdot$$1 (8$$\cdot$$0) 5$$\cdot$$9 (10$$\cdot$$9) 7$$\cdot$$3 (9$$\cdot$$9) 5 $$\lambda_1$$ 1$$\cdot$$4 (1$$\cdot$$3) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 1$$\cdot$$3 (1$$\cdot$$2) 0$$\cdot$$8 (0$$\cdot$$7) $$\lambda_2$$ 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$2 (2$$\cdot$$7) 2$$\cdot$$3 (2$$\cdot$$7) 2$$\cdot$$4 (2$$\cdot$$8) 3$$\cdot$$4 (3$$\cdot$$1) Table 1 shows that our approach performs better than the fitted monotonized generalized additive model, unless the true function is smooth. The results also indicate a low sensitivity to $$\eta$$. In particular, higher values of $$\eta$$ yield improved results if the true function is smooth, as in Study 3, or requires a large number of points to be approximated, as in Study 4. Conversely, $$\eta=10$$ performs better in Study 1 and Study 2, as it does not tend to interpolate linearly when functions switch between a zero and nonzero slope. These findings are consistent with § 2: a higher value for $$\eta$$ tends to produce smoother estimates, as the sampled functions have more but smaller steps. 3.3. Sensitivity analysis on $$p$$ We consider $$K=3$$ regions with region 2 adjacent to regions 1 and 3 while region 1 and 3 are nonadjacent. Figure 4 shows the true functions $$(\lambda_1,\lambda_2, \lambda_3)$$, which all exhibit a discontinuity at $$(0{\cdot}5, 0{\cdot}5)$$ and are more similar for $$x_k\in[0,1]^2\setminus[0{\cdot}5,1{\cdot}0]^2$$ than for $$x_k\in[0{\cdot}5,1{\cdot}0]^2$$ ($$k=1,2,3$$). The distribution of $$x_k$$ ($$k=1,2,3$$) varies across three studies while $$\lambda_1,\lambda_2$$ and $$\lambda_3$$ remain unchanged. Specifically, the studies explore the performance of our approach, subject to the relative intensity of points in subsets of $$X$$ for which the functions are similar. Fig. 4. View largeDownload slide True functions $$(\lambda_1,\lambda_2,\lambda_3$$) in § 3.3. The $$\lambda_k(x)$$-axis ($$k=1,2,3$$) is from 0 to 3. Fig. 4. View largeDownload slide True functions $$(\lambda_1,\lambda_2,\lambda_3$$) in § 3.3. The $$\lambda_k(x)$$-axis ($$k=1,2,3$$) is from 0 to 3. We generate 200 data points for each region with variance $$\theta_k=0{\cdot}2^2$$ ($$k=1,2,3$$). The three studies vary with respect to the number of observations sampled on $$[0{\cdot}5,1{\cdot}0]^2$$ for regions 1 and 3, while $$x_2\sim\mbox{Un}([0,1]^2)$$ in all of them. Study 1 considers the case where $$x_k\sim\mbox{Un}([0,1]^2)$$ ($$k=1,3$$). In Study 2, 150 data points are sampled uniformly from $$[0{\cdot}5,1{\cdot}0]^2$$ for regions 1 and 3, while only 25 observations are sampled from this subset in Study 3. The remaining 175 and 50 data points in Study 2 and Study 3, respectively, are sampled uniformly from $$[0{\cdot}0,1{\cdot}0]^2\setminus[0{\cdot}5,1{\cdot}0]^2$$. We compare four settings for $$p$$. The first, $$p=1$$, yields the integrated squared difference in expression (2). Settings $$p=0{\cdot}2$$ and $$p=0{\cdot}6$$ allow for stronger dependence in the lower function values, while $$p=2$$ imposes increased dependence for higher function values. The other parameters are fixed at $$\eta=1000$$, $$d_{1,2}=d_{2,3}=1$$ and $$d_{1,3}=0$$. Table 2 shows that we improve upon $$\omega=0$$, except for $$p=2$$ in Study 3, and indicates sensitivity to the prior parameter $$p$$, as the settings with $$p<1$$ perform best. Since a setting of $$p<1$$ imposes higher dependence on the lower function values, we effectively borrow information across the functions in Fig. 4 to improve estimates for the lower function values without inducing a large bias on the upper function values. As such, our extended discrepancy measure based on (3) has benefits when compared to the integrated squared distance. Table 2 further indicates that the sensitivity to $$p$$ depends on where most of the data are observed: if data fall in areas where the functions differ, the sensitivity is lower. The individual summary statistics for each function are provided in the Supplementary Material. Table 2. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2,\lambda_3)$$ in Fig. 4 for the settings $$p=(1{\cdot}0, 0{\cdot}2, 0{\cdot}6, 2{\cdot}0)$$ and $$\omega=0$$ Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) Table 2. Mean $$(\times 100)$$ and standard deviation $$(\times 100)$$ of the absolute difference between the truth and posterior mean estimate for $$(\lambda_1,\lambda_2,\lambda_3)$$ in Fig. 4 for the settings $$p=(1{\cdot}0, 0{\cdot}2, 0{\cdot}6, 2{\cdot}0)$$ and $$\omega=0$$ Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) Study $$p=1{\cdot}0$$ $$p=0{\cdot}2$$ $$p=0{\cdot}6$$ $$p=2{\cdot}0$$ $$\omega=0$$ 1 5$${\cdot}$$9 (8$${\cdot}$$3) 5$${\cdot}$$3 (7$${\cdot}$$9) 5$${\cdot}$$2 (7$${\cdot}$$4) 5$${\cdot}$$8 (8$${\cdot}$$5) 6$${\cdot}$$3 (9$${\cdot}$$3) 2 6$${\cdot}$$3 (9$${\cdot}$$5) 5$${\cdot}$$9 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$0) 6$${\cdot}$$1 (10$${\cdot}$$1) 7$${\cdot}$$0 (11$${\cdot}$$9) 3 6$${\cdot}$$4 (8$${\cdot}$$8) 5$${\cdot}$$9 (9$${\cdot}$$0) 6$${\cdot}$$0 (9$${\cdot}$$1) 6$${\cdot}$$9 (9$${\cdot}$$7) 6$${\cdot}$$8 (9$${\cdot}$$6) 4. Case study We consider the Norwegian insurance and weather data used by Haug et al. (2011) and Scheel et al. (2013). The data provide the daily number of insurance claims due to precipitation, surface water, snow melt, undermined drainage, sewage back-flow or blocked pipes at municipality level from 1997 to 2006. The average number of policies held per month and multiple daily weather metrics, such as the amount of precipitation, are also recorded. Table 2 in Scheel et al. (2013) indicates that a generalized linear model underpredicts high numbers of claims, perhaps due to threshold effects, as the risk of localized flooding only exists for high daily precipitation levels. While linearity may be too strong an assumption, the risk per property increases with precipitation levels, motivating the application of our method. We consider the $$K=11$$ municipalities in Fig. 5 and explore the effect of the precipitation $$R_{k,t}$$ and $$R_{k,t-1}$$ ($$k=1,\ldots,11$$) on day $$t$$ and $$t-1$$, as Haug et al. (2011) and Scheel et al. (2013) find these to be the most informative explanatory variables. Fig. 5. View largeDownload slide Map of the Norwegian municipalities in § 4. Fig. 5. View largeDownload slide Map of the Norwegian municipalities in § 4. Let $$N_{k,t}$$ and $$A_{k,t}$$ denote the numbers of claims and policies, respectively, on day $$t$$ for municipality $$k$$. We model $$N_{k,t}$$ as binomial with the logit of the daily claim probability, $$p_{k,t}$$, given by $$\lambda_k\left(R_{k,t},R_{k,t-1}\right)$$. As in § 3, we define $$\lambda_k\left(R_{k,t}, R_{k,t-1}\right) = \mu_k + \varphi_k\left(R_{k,t},R_{k,t-1}\right)$$ and estimate $$\mu_k$$ and $$\varphi_k$$ ($$k=1,\ldots,11$$). Formally, \begin{equation*} N_{k,t} \sim \mbox{Bi}\left(A_{k,t}, p_{k,t}\right)\!,\qquad\mbox{logit} p_{k,t} = \mu_k + \varphi_k\left(R_{k,t},R_{k,t-1}\right)\!\text{.} \end{equation*} An intrinsic conditional autoregressive prior is defined for $$\mu_1, \ldots, \mu_{11}$$, and the boundaries of $$\varphi_k$$ ($$k=1,\ldots,11$$) are set to $$\delta_{\min}=0$$ and $$\delta_{\max}=10$$. The set $$X$$ is derived as the square spanned by the observed minima and maxima of $$R_{k,t}$$ across all municipalities and years. We set $$d_{k,k'}=1$$ in (4) if municipalities $$k$$ and $$k'$$ are adjacent and $$d_{k,k'}=0$$ otherwise. To avoid oversmoothing threshold effects, we select $$\eta=10$$, based on our results in § 3.2. The sensitivity analysis in § 3.3 motivates setting $$p<1$$ since we expect the municipalities to exhibit similar vulnerability to small amounts of precipitation, while differences in infrastructure, for example, may lead to different effects for higher precipitation levels. We set $$p=0{\cdot}5$$. The functions $$\lambda_1,\ldots,\lambda_{11}$$ are estimated based on 1 000 000 iteration steps,with every 500th sample stored after a burn-in period of 200 000 iterations. To assess predictive performance, observations for 2001 and 2003 are stored as test data and $$\lambda_1,\ldots,\lambda_{11}$$ are estimated from the remaining eight years. We consider two competing models: (i) the average daily number of claims in the municipality over the training period; (ii) a linear model with spatially varying parameters (Assunção, 2003). The latter is estimated via 10 000 iterations of a random walk Metropolis scheme, with the first 1000 samples discarded. Table 3 shows that our approach is the best in terms of overall predictive performance. The small scale of improvement from $$\omega=0$$ to $$\omega=\omega_{\rm opt}$$ is due to the large number of training data points; important structures in $$\lambda_1,\ldots,\lambda_{11}$$ are likely to be captured without borrowing statistical information from adjacent municipalities. Posterior mean plots for Oslo and Hurum are provided in the Supplementary Material, but the function values are omitted for confidentiality. Table 3. Sum of squared errors of the daily number of claims in 2001 and 2003 for four models, with estimates being based on the remaining eight years between 1997 and 2006 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 Table 3. Sum of squared errors of the daily number of claims in 2001 and 2003 for four models, with estimates being based on the remaining eight years between 1997 and 2006 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 Municipality $$\omega=\omega_{\rm opt}$$ $$\omega=0$$ Constant Linear model Ås 14$$\cdot$$0 14$$\cdot$$1 13$$\cdot$$9 14$$\cdot$$3 Asker 360$$\cdot$$0 357$$\cdot$$7 372$$\cdot$$5 331$$\cdot$$0 Bærum 215$$\cdot$$2 234$$\cdot$$3 915$$\cdot$$1 679$$\cdot$$3 Frogn 8$$\cdot$$2 8$$\cdot$$2 8$$\cdot$$5 12$$\cdot$$3 Hurum 17$$\cdot$$4 17$$\cdot$$3 17$$\cdot$$7 17$$\cdot$$1 Nesodden 20$$\cdot$$7 20$$\cdot$$5 20$$\cdot$$5 20$$\cdot$$2 OppegÅrd 36$$\cdot$$1 36$$\cdot$$8 26$$\cdot$$2 27$$\cdot$$6 Oslo 440$$\cdot$$4 438$$\cdot$$3 412$$\cdot$$2 452$$\cdot$$3 Røyken 55$$\cdot$$9 56$$\cdot$$5 63$$\cdot$$5 53$$\cdot$$3 Ski 39$$\cdot$$1 39$$\cdot$$2 38$$\cdot$$2 38$$\cdot$$8 Vestby 18$$\cdot$$7 18$$\cdot$$8 18$$\cdot$$5 18$$\cdot$$9 Overall 1225$$\cdot$$7 1241$$\cdot$$7 1906$$\cdot$$8 1665$$\cdot$$1 The largest improvement is achieved for Bærum, which has the highest $$N_{k,t}$$ over the test period. Hence, the increased flexibility of our approach captures the dynamics leading to large numbers of claims better than competing models. For the other municipalities, the models perform similarly, due to there being zero high-claim days over the test period. This is also indicated by the predictive error of the constant mean model being low for most municipalities. Our approach performs slightly worse than the linear model for Asker, which is due to a single observation $$N_{k,t}$$. In particular, high precipitation levels caused a count $$N_{k,t}$$ which was the highest over the full 10-year period. 5. Discussion Our modelling framework can be extended to a spatio-temporal context. Assume that the intercept changes between observations but the effect of the explanatory variables is temporally stationary. We can then define $$\lambda_{k,t}\left(x\right) = \mu_{k,t} + \varphi_{k}\left(x\right)$$ ($$k=1,\ldots,K$$), similar to § 3 and § 4. Temporal structure on $$\mu_{k,1},\ldots,\mu_{k,T_k}$$ is, for instance, imposed via an autoregressive model. Our approach can also be extended to a setting for which $$\lambda_1,\ldots,\lambda_K$$ change at specified time-points, with temporal structure being imposed analogously to the spatial structure using time-adjacency. An aspect not discussed is the selection of the number $$I$$ of marked point processes representing $$\lambda_k$$ ($$k=1,\ldots,K$$). Since we considered examples with $$m=2$$ explanatory variables, $$I=3$$. In higher dimensions, however, one may want to restrict $$I$$. Assume there exists prior knowledge that continuous variable $$x_{k,h}$$ ($$h=1,\ldots,m$$) is informative and let $$X=[0,1]^m$$. The set of processes could then be defined based on the nonempty subsets of $$\left\{1,\ldots,m\right\}$$ which contain $$h$$. Consequently, we would represent $$\lambda_k$$ ($$k=1\ldots,K$$) via $$2^{m-1}$$, instead of $$2^{m}-1$$, processes. Our method performs well for regression problems with $$m=2$$ to $$m=5$$ explanatory variables. However, as for other flexible approaches, such as generalized additive models, some issues arise in higher dimensions. Firstly, the computational cost of calculating the prior ratio grows exponentially with $$m$$. We reduce this cost by deriving the subset of $$X$$ affected by the proposal before evaluating the integral in expression (4). Secondly, the monotonicity constraint becomes less restrictive with increasing dimension, leading to potential overfitting. Larger sets of explanatory variables can be accommodated by imposing an additive or semiparametric structure on $$\lambda_k$$ ($$k=1,\ldots,K$$), where the lower-dimensional monotonic functions are then estimated jointly. Consequently, our method can be applied to higher-dimensional regression problems, but we would recommend a pre-analysis. Our work can be extended in several ways, such as to the construction of other discrepancy measures based, for instance, on the Kullback–Leibler divergence. When estimating $$\omega$$, parallelized computing techniques, allocating the folds to multiple processors, can reduce the computational time. Further, we arbitrarily fixed the number of folds to $$s=10$$, but the value for $$\omega$$ also depends on the number of data points. A larger number of folds may return a more robust estimate. Acknowledgement Rohrbeck gratefully acknowledges funding from the U.K. Engineering and Physical Sciences Research Council. This work was also financially supported by the Norwegian Research Council. We greatly benefited from discussions with colleagues at Lancaster University, Elija Arjas and Sylvia Richardson. We also thank Ida Scheel for providing access to the insurance and weather data, and the editors and two referees for suggestions that substantially improved the presentation of the paper. Supplementary material Supplementary material available at Biometrika online contains a dependence model for functions with varying support, details on the prior and the sampling scheme, the proofs of Proposition 1 and Theorem 1, an algorithm to detect discontinuities, further material related to the analysis in § 3 and 4 and additional simulation studies. Appendix Details of the sampling scheme for the marked point processes We present the acceptance probabilities for the three moves in § 2.5; more details are provided in the Supplementary Material. For notational simplicity, let birth and death moves be proposed with equal probability and let $$\Delta_{k,i^*}$$ ($$k=1,\ldots,K; i^*=1,\ldots,I$$) denote the marked point process to be updated. A birth move proposes the addition of a point $$\left(\xi^*, \delta^*\right)$$ to $$\Delta_{k,i^*}$$. Since this increases the dimension of the parameter space, the acceptance probability has to be derived as described by Green (1995). The mapping for adding a point is equal to the identity function and, hence, the determinant of the Jacobian in the acceptance probability is equal to 1. Further, the proposal densities $$q(\xi^*)$$ and $$q\left(\delta^*\mid\xi^*,\Delta_k\right)$$ cancel with parts of the prior $$\phi\left(\Delta_k\mid\eta\right)$$. Formally, the acceptance probability is \begin{equation*} \min\!\!\left(\!1,\prod_{t=1}^{T_k}\frac{f\left\{\,y_{k,t}\mid \lambda_k^*(x_{k,t}),\theta_k\right\}}{f\left\{\,y_{k,t}\mid \lambda_k(x_{k,t}),\theta_k\right\}} \times \prod_{k'\neq k} \frac{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda^*_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} {\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} \times \left(1-\frac{1}{\eta}\right)\frac{N_k+1}{N_k+I}\!\right)\!\text{.} \end{equation*} A death or shift move is rejected if $$\Delta_{k,i^*}$$ contains no points. Otherwise, a death move selects one of the $$n_{k,i^*}$$ existing points with equal probability and proposes to remove it. The acceptance probability for a death move is then \begin{equation*} \min\!\!\left(\!1,\prod_{t=1}^{T_k}\frac{f\big\{\,y_{k,t}\mid \tilde\lambda_k^*(x_{k,t}),\theta_k\big\}}{f\big\{\,y_{k,t}\mid \tilde\lambda_k (x_{k,t}),\theta_k\big\}} \times \prod_{k'\neq k} \frac{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda^*_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} {\exp\big[{-}\omega d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]} \times\frac{1}{1-\frac{1}{\eta}}\frac{N_k+I-1}{N_k}\!\right)\!\text{.} \end{equation*} Finally, a shift move changes both the location and mark of an existing point, subject to the partial ordering of the locations in $$\Delta_{k,1},\ldots,\Delta_{k,I}$$, induced by the monotonicity constraint, being maintained. First, one of the $$n_{k,i^*}$$ points in $$\Delta_{k,i^*}$$ is selected with equal probability. The proposed location $${\xi}^*$$ is then sampled uniformly on the subset of $$X_i$$ which maintains the total order in each component of the locations; see Saarela & Arjas (2011) for details. The proposed mark $$\delta^*$$ is then sampled uniformly, subject to the monotonicity constraints. Formally, the acceptance probability is \begin{equation*} \min\left(1, \prod_{t=1}^{T_k}\frac{f\big\{\,y_{k,t}\mid\tilde\lambda_k(x_{k,t}),\theta_k\big\}}{f\big\{\,y_{k,t}\mid\tilde\lambda_k(x_{k,t}),\theta_k\big\}} \times \prod_{k'\neq k} \frac{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x)\big\}\,\mbox{d}x\big]}{\exp\big[{-}{\omega} d_{k,k'}\!\int_{X}\gamma_{p}\big\{\tilde\lambda_k(x),\tilde\lambda_{k'}(x) \big\}\,\mbox{d}x\big]}\right)\!\text{.} \end{equation*} References Andrieu C. & Roberts G. O. ( 2009 ). The pseudo-marginal approach for efficient Monte Carlo computations. Ann. Statist. 37 , 697 – 725 . Google Scholar CrossRef Search ADS Assunçço R. M. ( 2003 ). Space varying coefficient models for small area data. Environmetrics 14 , 453 – 73 . Google Scholar CrossRef Search ADS Ayer M., Brunk H. D., Ewing G. M., Reid W. T. & Silverman E. ( 1955 ). An empirical distribution function for sampling with incomplete information. Ann. Math. Statist. 26 , 641 – 7 . Google Scholar CrossRef Search ADS Bacchetti P. ( 1989 ). Additive isotonic model. J. Am. Statist. Assoc. 84 , 289 – 94 . Barlow R. & Brunk H. ( 1972 ). The isotonic regression problem and its dual. J. Am. Statist. Assoc. 67 , 140 – 7 . Google Scholar CrossRef Search ADS Barron A., Schervish M. J. & Wasserman L. ( 1999 ). The consistency of posterior distributions in nonparametric problems. Ann. Statist. 27 , 536 – 61 . Google Scholar CrossRef Search ADS Beaumont M. A., Zhang W. & Balding D. J. ( 2002 ). Approximate Bayesian computation in population genetics. Genetics 162 , 2025 – 35 . Google Scholar PubMed Bell M. L., McDermott A., Zeger S. L., Samet J. M. & Dominici F. ( 2004 ). Ozone and short-term mortality in 95 US urban communities, 1987–2000. J. Am. Med. Assoc. 292 , 2372 – 8 . Google Scholar CrossRef Search ADS Bergersen L. C., Tharmaratnam K. & Glad I. K. ( 2014 ). Monotone splines lasso. Comp. Statist. Data Anal. 77 , 336 – 51 . Google Scholar CrossRef Search ADS Besag J., York J. & Mollié A. ( 1991 ). Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Statist. Math. 43 , 1 – 20 . Google Scholar CrossRef Search ADS Bowman A. W. & Azzalini A. 1997 . Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations . Oxford : Clarendon Press . Bowman A. W., Jones M. C. & Gijbels I. ( 1998 ). Testing monotonicity of regression. J. Comp. Graph. Statist. 7 , 489 – 500 . Brunk H. D. ( 1955 ). Maximum likelihood estimates of monotone parameters. Ann. Math. Statist. 26 , 607 – 16 . Google Scholar CrossRef Search ADS Brunk H. D., Ewing G. M. & Utz W. R. ( 1957 ). Minimizing integrals in certain classes of monotone functions. Pac. J. Math. 7 , 833 – 47 . Google Scholar CrossRef Search ADS Cahill M. & Mulligan G. ( 2007 ). Using geographically weighted regression to explore local crime patterns. Social Sci. Comp. Rev. 25 , 174 – 93 . Google Scholar CrossRef Search ADS Congdon P. ( 2006 ). A model for non-parametric spatially varying regression effects. Comp. Statist. Data Anal. 50 , 422 – 45 . Google Scholar CrossRef Search ADS Fang Z. & Meinshausen N. ( 2012 ). LASSO isotone for high-dimensional additive isotonic regression. J. Comp. Graph. Statist. 21 , 72 – 91 . Google Scholar CrossRef Search ADS Farah M., Kottas A. & Morris R. D. ( 2013 ). An application of semiparametric Bayesian isotonic regression to the study of radiation effects in spaceborne microelectronics. Appl. Statist. 62 , 3 – 24 . Fotheringham A. S., Brunsdon C. & Charlton M. 2003 . Geographically Weighted Regression: The Analysis of Spatially Varying Relationships . Hoboken, New Jersey : Wiley . Gelfand A. E. & Kuo L. ( 1991 ). Nonparametric Bayesian bioassay including ordered polytomous response. Biometrika 78 , 657 – 66 . Google Scholar CrossRef Search ADS Georgii H.-O. 2011 . Gibbs Measures and Phase Transitions . Berlin : de Gruyter , 2nd ed. Ghosal S., Sen A. & van der Vaart A. W. ( 2000 ). Testing monotonicity of regression. Ann. Statist. 28 , 1054 – 82 . Google Scholar CrossRef Search ADS Green P. J. ( 1995 ). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82 , 711 – 32 . Google Scholar CrossRef Search ADS Hastie T. J. & Tibshirani R. J. 1990 . Generalized Additive Models . Boca Raton, Florida : Chapman & Hall. Haug O., Dimakos X. K., Vårdal F., J., Aldrin M. & Meze-Hausken E. ( 2011 ). Future building water loss projections posed by climate change. Scand. Actuar. J. 2011 , 1 – 20 . Google Scholar CrossRef Search ADS Heikkinen J. & Arjas E. ( 1998 ). Non-parametric Bayesian estimation of a spatial Poisson intensity. Scand. J. Statist. 25 , 435 – 50 . Google Scholar CrossRef Search ADS Jones D. R., Schonlau M. & Welch W. J. ( 1998 ). Efficient global optimization of expensive black-box functions. J. Global Optimiz. 13 , 455 – 92 . Google Scholar CrossRef Search ADS Knorr-Held L. 2003 . Some remarks on Gaussian Markov random field models for disease mapping. In Highly Structured Stochastic Systems , Green P. J. Hjort N. L. & Richardson S. eds. Oxford : Oxford University Press , pp. 203 – 7 . Lin L. & Dunson D. B. ( 2014 ). Bayesian monotone regression using Gaussian process projection. Biometrika 101 , 303 – 17 . Google Scholar CrossRef Search ADS Luss R., Rosset S. & Shahar M. ( 2012 ). Efficient regularized isotonic regression with application to gene–gene interaction search. Ann. Appl. Statist. 6 , 253 – 83 . Google Scholar CrossRef Search ADS Møller J., Pettitt A. N., Reeves R. & Berthelsen K. K. ( 2006 ). An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika 93 , 451 – 8 . Google Scholar CrossRef Search ADS Penttinen A., Stoyan D. & Henttonen H. M. ( 1992 ). Marked point processes in forest statistics. Forest Sci. 38 , 806 – 24 . Ramsay J. O. 1998 . Estimating smooth monotone functions. J. R. Statist. Soc. B 60 , 365 – 75 . Google Scholar CrossRef Search ADS Ramsay J. O. & Silverman B. W. 2005 . Functional Data Analysis . New York : Springer , 2nd ed . Roustant O., Ginsbourger D. & Deville Y. ( 2012 ). DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J. Statist. Software 51 , 1 – 55 . Google Scholar CrossRef Search ADS Royston P. ( 2000 ). A useful monotonic non-linear model with applications in medicine and epidemiology. Statist. Med. 19 , 2053 – 66 . Google Scholar CrossRef Search ADS Rue H. & Held L. 2005 . Gaussian Markov Random Fields: Theory and Applications . Boca Raton, Florida : Chapman & Hall . Saarela O. & Arjas E. ( 2011 ). A method for Bayesian monotonic multiple regression. Scand. J. Statist. 38 , 499 – 513 . Scheel I., Ferkingstad E., Frigessi A., Haug O., Hinnerichsen M. & Meze-Hausken E. ( 2013 ). A Bayesian hierarchical model with spatial variable selection: The effect of weather on insurance claims. Appl. Statist. 62 , 85 – 100 . Scott J. G., Shively T. S. & Walker S. G. ( 2015 ). Nonparametric Bayesian testing for monotonicity. Biometrika 102 , 617 – 30 . Google Scholar CrossRef Search ADS Shively T. S., Sager T. W. & Walker S. G. 2009 . A Bayesian approach to non-parametric monotone function estimation. J. R. Statist. Soc. B 71 , 159 – 75 . Google Scholar CrossRef Search ADS Tutz G. & Leitenstorfer F. ( 2007 ). Generalized smooth monotonic regression in additive modeling. J. Comp. Graph. Statist. 16 , 165 – 88 . Google Scholar CrossRef Search ADS Wakefield J. ( 2007 ). Disease mapping and spatial regression with count data. Biostatistics 8 , 158 – 83 . Google Scholar CrossRef Search ADS PubMed Walker S. G. & Hjort N. L. 2001 . On Bayesian consistency. J. R. Statist. Soc. B 63 , 811 – 21 . Google Scholar CrossRef Search ADS Waller L. A. & Gotway C. A. 2004 . Applied Spatial Statistics for Public Health Data . Hoboken, New Jersey : Wiley . Wilson A., Reif D. M. & Reich B. J. ( 2014 ). Hierarchical dose–response modeling for high-throughput toxicity screening of environmental chemicals. Biometrics 70 , 237 – 46 . Google Scholar CrossRef Search ADS PubMed Zhang L. & Shi H. ( 2004 ). Local modeling of tree growth by geographically weighted regression. Forest Sci. 50 , 225 – 44 . © 2018 Biometrika Trust This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Published: Sep 1, 2018

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