Bayesian Variable Selection for Latent Class Models

Authors

  • Joyee Ghosh,

    Corresponding author
    1. Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, Iowa 52242, U.S.A.
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  • Amy H. Herring,

    Corresponding author
    1. Department of Biostatistics and Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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  • Anna Maria Siega-Riz

    Corresponding author
    1. Department of Epidemiology, Department of Nutrition and Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.
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email:joyee-ghosh@uiowa.edu

email:amy_herring@unc.edu

email:am_siegariz@unc.edu

Abstract

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.

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