JSM 2005 Online Program

Abstract #302354

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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 - #302354
Title:	Bayesian Nonparametric Regression Analysis when Covariates Are Subject-specific Parameters in a Random-effects Model for Longitudinal Measurements
Author(s):	Bani Mallick*+
Companies:	Texas A&M University
Address:	Department of Statistics, College Station, TX, 77843-3143,
Keywords:	Bayesian smoothing spline ; Generalized linear model ; Latent variable ; Measurement error ; MCMC
Abstract:	In this paper, we consider nonparametric regression analysis in a generalized linear model framework with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. A naive approach replaces the covariate variables by subject-specific estimated coefficients and yields biased inference. Several bias reduction techniques have been proposed for linear regression problems. The assumption that the effect of the covariate is linear is often unrealistic. We relax this linearity assumption using a Bayesian cubic smoothing spline model. With the presence of several covariates, we use an additive model in this complex setting. The posterior model space is explored using a Markov Chain Monte Carlo (MCMC) sampler. The method is illustrated via simulations and by application to a child growth study.

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