Approximate uniform shrinkage prior for a multivariate generalized linear mixed model

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• 作者：Hsiang-Chun Chen^a ; ^{hchen8@mdanderson.org" class="auth_mail" title="E-mail the corresponding author} ; Thomas E. Wehrly^b
• 关键词：Bayesian inference ; Multivariate generalized linear model ; Noninformative prior distribution ; Uniform shrinkage prior
• 刊名：Journal of Multivariate Analysis
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：145
• 期：Complete
• 页码：148-161
• 全文大小：405 K

文摘

Multivariate generalized linear mixed models (MGLMM) are used for jointly modeling the clustered mixed outcomes obtained when there are two or more responses repeatedly measured on each individual in scientific studies. Bayesian methods are widely used techniques for analyzing MGLMM. The need for noninformative priors arises when there is insufficient prior information on the model parameters. The main aim of the present study is to propose an approximate uniform shrinkage prior for the random effect variance components in the Bayesian analysis for the MGLMM. This prior is an extension of the approximate uniform shrinkage prior proposed by Natarajan and Kass (2000). This prior is easy to apply and is shown to possess several nice properties. The use of the approximate uniform shrinkage prior is illustrated in terms of both a simulation study and osteoarthritis data.