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Bayesian Variable Selection for Latent Class Models
Author(s) -
Ghosh Joyee,
Herring Amy H.,
SiegaRiz Anna Maria
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01502.x
Subject(s) - latent class model , gibbs sampling , covariate , latent variable , latent variable model , bayesian probability , selection (genetic algorithm) , computer science , econometrics , statistics , posterior probability , class (philosophy) , model selection , mathematics , machine learning , artificial intelligence
Summary 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.