Overfitting Bayesian mixtures of factor analyzers with an unknown number of components

DOI10.1016/j.csda.2018.03.007zbMath1469.62125arXiv1701.04605OpenAlexW2572696010MaRDI QIDQ140406

Panagiotis Papastamoulis

Publication date: August 2018

Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.04605

zbMATH Keywords

clusteringfactor analysismixture modelsMCMC

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)

Related Items

Infinite mixtures of infinite factor analysers, Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components, On the identifiability of Bayesian factor analytic models, fabMix, On Bayesian analysis of parsimonious Gaussian mixture models, Model-based clustering of censored data via mixtures of factor analyzers

Uses Software

• R
• UCI-ml
• PGMM
• corrplot
• Label.switching
• BayesBinMix
• poisson.glm.mix
• EMMIXmfa

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