Bayesian Inferences of Latent Class Models with an Unknown Number of Classes

Jia-Chiun Pan and Guan-Hua Huang ()

Psychometrika, 2014, vol. 79, issue 4, 646 pages

Abstract: This paper focuses on analyzing data collected in situations where investigators use multiple discrete indicators as surrogates, for example, a set of questionnaires. A very flexible latent class model is used for analysis. We propose a Bayesian framework to perform the joint estimation of the number of latent classes and model parameters. The proposed approach applies the reversible jump Markov chain Monte Carlo to analyze finite mixtures of multivariate multinomial distributions. In the paper, we also develop a procedure for the unique labeling of the classes. We have carried out a detailed sensitivity analysis for various hyperparameter specifications, which leads us to make standard default recommendations for the choice of priors. The usefulness of the proposed method is demonstrated through computer simulations and a study on subtypes of schizophrenia using the Positive and Negative Syndrome Scale (PANSS). Copyright The Psychometric Society 2014

Keywords: categorical data; finite mixture model; label switching; reversible jump Markov chain Monte Carlo; sensitivity analysis; surrogate endpoint (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11336-013-9368-7

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