Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler
Refereed Original Article
Latent class analysis is used to perform model basedclusteringformultivariatecategoricalresponses.Selec- tion of the variables most relevant for clustering is an impor- tant task which can affect the quality of clustering consider- ably. This work considers a Bayesian approach for selecting the number of clusters and the best clustering variables. The main idea is to reformulate the problem of group and variable selection as a probabilistically driven search over a large dis- cretespaceusingMarkovchainMonteCarlo(MCMC)meth- ods.Bothselectiontasksarecarriedoutsimultaneouslyusing an MCMC approach based on a collapsed Gibbs sampling method, whereby several model parameters are integrated from the model, substantially improving computational per- formance.Post-hocproceduresforparameteranduncertainty estimation are outlined. The approach is tested on simulated and real data .
Digital Object Identifer (DOI):
Statistics and Computing
National University of Ireland, Dublin (UCD)
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