JSM 2005 Online Program

Abstract #303591

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Activity Number:	92
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303591
Title:	Bayesian Analysis of the Mixed Models for Repeated Binary Response and Time-Dependent Missing Covariates
Author(s):	Lan Huang*+ and Ming-Hui Chen and Paul R. Neal and Gregory J. Anderson
Companies:	National Cancer Institute and University of Connecticut and University of Connecticut and University of Connecticut
Address:	Rm 5043, Rockville, MD, 20852, United States
Keywords:	Generalized linear mixed model (GLMM) ; Time-dependent missing covariates ; Repeated binary responses ; MCMC algorithm ; DIC ; Periodical cycle of flower intensity
Abstract:	In this paper, we develop a novel modeling strategy for analyzing time-dependent missing covariates and data with repeated binary responses over time. We use the generalized linear mixed logistic regression model for the repeated binary responses and propose a joint model for time-dependent missing covariates using information from different sources. We develop an efficient Gibbs sampling algorithm to sample from the joint posterior distribution. Moreover, we propose a novel Monte Carlo method to compute a Bayesian model comparison criterion, DIC, to identify factors such as defoliation by gypsy moths and weather conditions that may disrupt the cyclical pattern of flowering.

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