Estimation and model selection for model-based clustering with the conditional classification likelihood

DOI10.1214/15-EJS1026zbMath1325.62120arXiv1205.4123MaRDI QIDQ2346523

Jean-Patrick Baudry

Publication date: 2 June 2015

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1205.4123

zbMATH Keywords

model selectionmodel-based clusteringbracketing entropyICLnumber of classespenalized criteria

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

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Uses Software

• CAPUSHE

Cites Work

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• Model-based clustering and classification with non-normal mixture distributions
• Methods for merging Gaussian mixture components
• Exact posterior distributions and model selection criteria for multiple change-point detection problems
• Slope heuristics: overview and implementation
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Uncovering latent structure in valued graphs: a variational approach
• Estimating the dimension of a model
• Risk bounds for model selection via penalization
• Maximum likelihood principle and model selection when the true model is unspecified
• Model selection by resampling penalization
• Minimal penalties for Gaussian model selection
• Nonparametric estimation of regression level sets using kernel plug-in estimator
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Clustering Criteria and Multivariate Normal Mixtures
• Uniform Central Limit Theorems
• Asymptotic Statistics
• Numerical Analysis for Statisticians
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• A non asymptotic penalized criterion for Gaussian mixture model selection
• Gaussian model selection