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

Jia Chiun Pan, Guan-Hua Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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).

Original languageEnglish
Pages (from-to)621-646
Number of pages26
Issue number4
StatePublished - 1 Jan 2014


  • categorical data
  • finite mixture model
  • label switching
  • reversible jump Markov chain Monte Carlo
  • sensitivity analysis
  • surrogate endpoint


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