Journal article
Bayesian variable selection for latent class models
Biometrics, Vol.67(3), pp.917-925
09/2011
DOI: 10.1111/j.1541-0420.2010.01502.x
PMCID: PMC3035762
PMID: 21039399
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.
Details
- Title: Subtitle
- Bayesian variable selection for latent class models
- Creators
- Joyee Ghosh - Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, Iowa 52242, USA. joyee-ghosh@uiowa.eduAmy H HerringAnna Maria Siega-Riz
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.67(3), pp.917-925
- DOI
- 10.1111/j.1541-0420.2010.01502.x
- PMID
- 21039399
- PMCID
- PMC3035762
- NLM abbreviation
- Biometrics
- ISSN
- 1541-0420
- eISSN
- 1541-0420
- Publisher
- United States
- Grant note
- HD39373 / NICHD NIH HHS P30ES10126 / NIEHS NIH HHS R01 HD039373 / NICHD NIH HHS R01 DK061981 / NIDDK NIH HHS R40MC08952 / PHS HHS DK61981 / NIDDK NIH HHS T32 ES007018-33 / NIEHS NIH HHS P30 ES010126 / NIEHS NIH HHS T32 ES007018 / NIEHS NIH HHS R24 HD050924 / NICHD NIH HHS 5T32ES007018 / NIEHS NIH HHS HD37584 / NICHD NIH HHS HD28684 / NICHD NIH HHS R01 HD037584 / NICHD NIH HHS
- Language
- English
- Date published
- 09/2011
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985972302771
Metrics
39 Record Views