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

Published online by Cambridge University Press:  01 January 2025

Jia-Chiun Pan  and
Guan-Hua Huang
Jia-Chiun Pan
Affiliation:
National Chung Cheng University
Guan-Hua Huang*
Affiliation:
National Chiao Tung University
*
Requests for reprints should be sent to Guan-Hua Huang, Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 30010, Taiwan. E-mail: ghuang@stat.nctu.edu.tw
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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).

Keywords

categorical datafinite mixture modellabel switchingreversible jump Markov chain Monte Carlosensitivity analysissurrogate endpoint
Type
Original Paper
Information
Psychometrika , Volume 79 , Issue 4 , October 2014 , pp. 621 - 646
Copyright
Copyright © 2013 The Psychometric Society

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Footnotes

Electronic Supplementary Material The online version of this article (doi:10.1007/s11336-013-9368-7) contains supplementary material, which is available to authorized users.

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