JSM 2005 Online Program

Abstract #304514

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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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 - #304514
Title:	Flexible Modeling of the Dependence Structure in Missing Data Problems
Author(s):	John Boscardin*+ and Xiao Zhang
Companies:	University of California, Los Angeles and University of California, Los Angeles
Address:	51254 CHS Mailcode 177220, Los Angeles, CA, 90095-1772, United States
Keywords:	Markov chain Monte Carlo ; covariance structure ; repeated measures ; multivariate probit model ; multinomial probit model
Abstract:	MCMC computational methods have facilitated a large amount of recent research in Bayesian models for the dependence structure of repeated measures (continuous, ordinal, and categorical). The models we have developed can be thought of as providing a continuous bridge between parametric and unstructured covariance matrices. We have shown that these models have the right amount of data-determined flexibility to perform well in terms of estimation of the covariance matrix. As these models can be easily augmented to treat missing data, it is natural to examine their advantages from an imputation standpoint. We present illustrative examples for inference on subject-specific and population mean parameters and model selection. An application to modeling longterm outcome of severe head trauma patients also will be presented.

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