title: Comparison of Criteria for Choosing the Number of Classes in Bayesian Finite Mixture Models.
abstract: Identifying the number of classes in Bayesian finite mixture models is a challenging problem. Several criteria have been proposed, such as adaptations of the deviance information criterion, marginal likelihoods, Bayes factors, and reversible jump MCMC techniques. It was recently shown that in overfitted mixture models, the overfitted latent classes will asymptotically become empty under specific conditions for the prior of the class proportions. This result may be used to construct a criterion for finding the true number of latent classes, based on the removal of latent classes that have negligible proportions. Unlike some alternative criteria, this criterion can easily be implemented in complex statistical models such as latent class mixed-effects models and multivariate mixture models using standard Bayesian software. We performed an extensive simulation study to develop practical guidelines to determine the appropriate number of latent classes based on the posterior distribution of the class proportions, and to compare this criterion with alternative criteria. The performance of the proposed criterion is illustrated using a data set of repeatedly measured hemoglobin values of blood donors.
authors.affiliation: Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands.|Department of Hematology, Clinical Trial Center, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
authors.forename: Kazem
authors.initials: K
authors.lastname: Nasserinejad
authors.affiliation: Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands.
authors.forename: Joost
authors.initials: J
authors.lastname: van Rosmalen
authors.affiliation: Sanquin Research, Department of Donor Studies, Amsterdam, the Netherlands.|Department of Public Health, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Wim
authors.initials: W
authors.lastname: de Kort
authors.affiliation: L-Biostat, KU Leuven, Leuven, Belgium.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Lesaffre
mesh_terms: D001499:Bayes Theorem; D008962:Models, Theoretical; D012984:Software