Title: Bayesian variable selection for latent class models.

Authors: Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

Published In Biometrics, (2011 Sep)

Abstract: In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

PubMed ID: 21039399

MeSH Terms: No MeSH terms associated with this publication