Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler

DOI10.1007/s11222-014-9542-5zbMath1342.62112arXiv1402.6928OpenAlexW1990158041MaRDI QIDQ2631382

Jason Wyse, Thomas Brendan Murphy, Arthur T. White

Publication date: 29 July 2016

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.6928

zbMATH Keywords

model selectionfinite mixture modelvariable selectionlatent class analysistrans-dimensional MCMCcollapsed sampler

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10) Bayesian inference (62F15)

Related Items

Simultaneous variable weighting and determining the number of clusters -- a weighted Gaussian means algorithm, A hierarchical Bayesian approach for examining heterogeneity in choice decisions, A model-based approach to simultaneous clustering and dimensional reduction of ordinal data, Bayesian inference for an unknown number of attributes in restricted latent class models, An Overview on the URV Model-Based Approach to Cluster Mixed-Type Data, Variable selection methods for model-based clustering, Optimal Bayesian estimators for latent variable cluster models, Variable selection for model-based clustering using the integrated complete-data likelihood, Variable selection for mixed data clustering: application in human population genomics

Uses Software

• R
• CODA
• tsbridge
• BayesLCA
• Algorithm 548

Cites Work

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