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We consider kernel-type methods for the estimation of a density on 0,1 which eschew explicit boundary correction. We propose using kernels that are symmetric in their two arguments; these kernels are conditional densities of bivariate copulas. We give asymptotic theory for the version of the new...
Most statistical solutions to the problem of statistical inference with missing data involve integration or expectation. This can be done in many ways: directly or indirectly, analytically or numerically, deterministically or stochastically. Missing-data problems can be formulated in terms of...
Chen & Dunson (( 3 )) have proposed a modified Cholesky decomposition of the form σ = D L L ′D for a covariance matrix where D is a diagonal matrix with entries proportional to the square roots of the diagonal entries of Σ and L is a unit lower-triangular matrix solely determining its...
We show that, when the three-way association level among the three binary variables, X , U 1 and U 2 is fixed, D P = pr(X = 1¦ U 1 = 1) − pr( X = 1¦ U 1 = 0) increases as the cross-product ratio of U 1 and U 2 increases under the assumption that X is positively associated with U 1 and U 2 ....
We propose a robust curve and surface estimator based on M -type estimators and penalty-based smoothing. This approach also includes an application to wavelet regression. The concept of pseudo data, a transformation of the robust additive model to the one with bounded errors, is used to derive...
A modification to the pairwise likelihood method is proposed, which aims to improve the estimation of the marginal distribution parameters. This is achieved by replacing the pairwise likelihood score equations, for estimating such parameters, by the optimal linear combinations of the marginal...
We develop a proposal or importance density for state space models with a nonlinear non-Gaussian observation vector y ∼ p ( y ¦θ) and an unobserved linear Gaussian signal vector θ ∼ p (θ). The proposal density is obtained from the Laplace approximation of the smoothing density p (θ¦ y...
A random sample is drawn from a distribution which admits a minimal sufficient statistic for the parameters. The Gibbs sampler is proposed to generate samples, called conditionally sufficient or co-sufficient samples, from the conditional distribution of the sample given its value of the...
Very often in survival analysis one has to study martingale integrals where the integrand is not predictable and where the counting process theory of martingales is not directly applicable, as for example in nonparametric and semiparametric applications where the integrand is based on a pilot...
We consider the problem of evaluating the probability of discovering a certain number of new species in a new sample of population units, conditional on the number of species recorded in a basic sample. We use a Bayesian nonparametric approach. The different species proportions are assumed to be...
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