Bayesian variable selection for latent class models.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Ghosh, J; Herring, AH; Siega-Riz, AM

Published Date

  • September 2011

Published In

Volume / Issue

  • 67 / 3

Start / End Page

  • 917 - 925

PubMed ID

  • 21039399

Pubmed Central ID

  • 21039399

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2010.01502.x

Language

  • eng