Abstract #301566

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JSM 2003 Abstract #301566
Activity Number: 173
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301566
Title: Random Effects Selection in Linear Mixed-Effects Models
Author(s): David B. Dunson*+ and Zhen Chen
Companies: National Institute of Environmental Health Sciences and National Institute of Environmental Health Science
Address: Biostatistics Branch, MD A3-03, Durham, NC, 27709-2233,
Keywords: latent variables ; longitudinal data ; variable selection ; MCMC ; model averaging
Abstract:

We address the important practical problem of how to select the random effects component in a linear mixed model. A hierarchical Bayesian model is used to identify any random effect with 0 variance. The proposed approach reparameterizes the mixed model so that functions of the covariance parameters of the random effects distribution are incorporated as regression coefficients on standard normal latent variables. We allow random effects to effectively drop out of the model by choosing mixture priors with point mass at zero for the random effects variances. Due to the reparameterization, the model enjoys a conditionally linear structure that facilitates the use of normal conjugate priors. We demonstrate that posterior computation can proceed via a simple and efficient Markov chain Monte Carlo algorithm. The methods are illustrated using simulated data and real data from a study relating prenatal exposure to polychlorinated biphenyls and psychomotor development of children.

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