Statistica Neerlandica

doi: 10.1111/stan.12117pmid: N/A

No abstract is available for this article.

Ly, Alexander; Marsman, Maarten; Wagenmakers, Eric‐Jan

doi: 10.1111/stan.12111pmid: 29353942

Pearson's correlation is one of the most common measures of linear dependence. Recently, Bernardo (11th International Workshop on Objective Bayes Methodology, 2015) introduced a flexible class of priors to study this measure in a Bayesian setting. For this large class of priors, we show that the (marginal) posterior for Pearson's correlation coefficient and all of the posterior moments are analytic. Our results are available in the open‐source software package JASP.

Hanea, A. M.; Nane, G. F.

doi: 10.1111/stan.12113pmid: N/A

Our main result gives the asymptotic distribution of the determinant of a random correlation matrix sampled in a particular way from the space of d×d correlation matrices. Several spin‐off results are proven along the way, and an interesting connection with the law of the determinant of general random matrices is investigated. As different methods for generating random correlation matrices are proposed in the literature, one application of our result is that in can be employed to differentiate between those methods.

Musoro, Jammbe Z; Zwinderman, Aeilko H; Abu‐Hanna, Ameen; Bosman, Rob; Geskus, Ronald B

doi: 10.1111/stan.12114pmid: N/A

In intensive care units (ICUs), besides routinely collected admission data, a daily monitoring of organ dysfunction using scoring systems such as the sequential organ failure assessment (SOFA) score has become practice. Such updated information is valuable in making accurate predictions of patients' survival. Few prediction models that incorporate this updated information have been reported. We used follow‐up data of ICU patients who either died or were discharged at the end of hospital stay, without censored cases. We propose a joint model comprising a linear mixed effects submodel for the development of longitudinal SOFA scores and a proportional subdistribution hazards submodel for death as end point with discharge as competing risk. The two parts are linked by shared latent terms. Because there was no censoring, it was straightforward to fit our joint model using available software. We compared predictive values, based on the Brier score and the area under the receiver operating characteristic curve, from our model with those obtained from an earlier modeling approach by Toma et al. [Journal of Biomedical Informatics 40, 649, (2007)] that relied on patterns discovered in the SOFA scores over a given period of time.

Wu, Sheng‐Jhih; Ghosh, Sujit K.; Ku, Yu‐Cheng; Bloomfield, Peter

doi: 10.1111/stan.12115pmid: N/A

Modeling the correlation structure of returns is essential in many financial applications. Considerable evidence from empirical studies has shown that the correlation among asset returns is not stable over time. A recent development in the multivariate stochastic volatility literature is the application of inverse Wishart processes to characterize the evolution of return correlation matrices. Within the inverse Wishart multivariate stochastic volatility framework, we propose a flexible correlated latent factor model to achieve dimension reduction and capture the stylized fact of ‘correlation breakdown’ simultaneously. The parameter estimation is based on existing Markov chain Monte Carlo methods. We illustrate the proposed model with several empirical studies. In particular, we use high‐dimensional stock return data to compare our model with competing models based on multiple performance metrics and tests. The results show that the proposed model not only describes historic stylized facts reasonably but also provides the best overall performance.