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Activity Number: 541
Type: Contributed
Date/Time: Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #309935
Title: A Semiparametric Bayesian Approach to the Generalized Linear Mixed Effects Model
Author(s): Jing Wang*+
Companies: Louisiana State University
Address: , Baton Rouge, LA, 70808,
Keywords: generalized linear mixed effects model ; Dirichlet process ; Gibbs sampler ; Monte Carlo methods
Abstract:

We describe and illustrate Bayesian analysis in the generalized linear mixed effects model that allows the random effects to have a nonparametric prior distribution, using mixtures of Dirichlet process prior for the distribution of the random effects. The computations are implemented by using the Gibbs sampler; computational difficulties involved in numerical integrations in complex multiparameter structures are solved by Monte Carlo methods. The Examples using real data are given to illustrate the methodology.

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Revised September, 2007