Approximate uniform shrinkage prior for a multivariate generalized linear mixed model Academic Article

abstract

• 2015 Elsevier Inc. 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.

authors

• Wehrly, Thomas

published proceedings

• JOURNAL OF MULTIVARIATE ANALYSIS

author list (cited authors)

• Chen, H., & Wehrly, T. E.

citation count

• 5

complete list of authors

• Chen, Hsiang-Chun||Wehrly, Thomas E

publication date

• March 2016

publisher

• Elsevier  Publisher

published in

• Journal of Multivariate Analysis  Journal

keywords

• Bayesian Inference
• Multivariate Generalized Linear Model
• Noninformative Prior Distribution
• Uniform Shrinkage Prior

Digital Object Identifier (DOI)

• 10.1016/j.jmva.2015.12.004

start page

• 148

end page

• 161

volume

• 145

issue

• C

URL

• http://dx.doi.org/10.1016/j.jmva.2015.12.004