JSM 2005 Online Program

Abstract #302649

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:	252
Type:	Invited
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Biometrics Section
Abstract - #302649
Title:	Bayesian Latent Variable Density Regression with Applications in Molecular Epidemiology
Author(s):	*+
Companies:	National Institute of Environmental Health Sciences
Address:	MD A3-03, NIEHS, P.O. Box 12233, Research Triangle Park, NC, 27709, USA
Keywords:	Nonparametric Bayes ; Dirichlet process ; Hierarchical model ; Random probability measure ; Dependent distributions ; Haplotypes
Abstract:	In hierarchical models with latent variables, there is uncertainty in the latent variable and/or outcome distributions, and these distributions can potentially change with predictors. For example, in studies of DNA damage and repair, the comet assay is used to obtain multiple surrogates of the frequency of DNA strand breaks for individual cells. The distributions of these surrogates do not follow standard parametric forms and can change in shape with genetic and environmental factors. This talk proposes semiparametric Bayesian methods for density regression based on a novel nonparametric approach, which allows a random probability distribution to change flexibly with multiple discrete and continuous predictors. The approach has a number of appealing practical and theoretical properties, which are illustrated through application to epidemiologic data.

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