Properties of prior and posterior distributions for multivariate categorical response data models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:71

Article

Chen, MH ; Shao, QM

关键词: Bayesian hierarchical model;    Markov chain Monte Carlo;    scale mixture of multivariate normal links;   

DOI  :  10.1006/jmva.1999.1846

来源: Elsevier

【 摘 要 】

In this article, we model multivariate categorical (binary and ordinal) response data using a very rich class of scale mixture of multivariate normal (SMMVN) link functions to accommodate heavy tailed distributions. We consider both noninformative as well as informative prior distributions for SMMVN-link models. The notation of informative prior elicitation is based on available similar historical studies. The main objectives of this article are (i) to derive theoretical properties of noninformative and informative priors as well as the resulting posteriors and (ii) to develop an efficient Markov chain Monte Carlo algorithm to sample from the resulting posterior distribution. A real data example from prostate cancer studies is used to illustrate the proposed methodologies. (C) 1999 Academic Press AMS 1991 subject classifications: primary 62A15; secondary 62H05, 62E15.

【 授权许可】

Free