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2013 Clustering and variable selection for categorical multivariate data
Dominique Bontemps, Wilson Toussile
Electron. J. Statist. 7: 2344-2371 (2013). DOI: 10.1214/13-EJS844


This article investigates unsupervised classification techniques for categorical multivariate data. The study employs multivariate multinomial mixture modeling, which is a type of model particularly applicable to multilocus genotypic data. A model selection procedure is used to simultaneously select the number of components and the relevant variables. A non-asymptotic oracle inequality is obtained, leading to the proposal of a new penalized maximum likelihood criterion. The selected model proves to be asymptotically consistent under weak assumptions on the true probability underlying the observations. The main theoretical result obtained in this study suggests a penalty function defined to within a multiplicative parameter. In practice, the data-driven calibration of the penalty function is made possible by slope heuristics. Based on simulated data, this procedure is found to improve the performance of the selection procedure with respect to classical criteria such as $\mathbf{BIC}$ and $\mathbf{AIC}$. The new criterion provides an answer to the question “Which criterion for which sample size?” Examples of real dataset applications are also provided.


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Dominique Bontemps. Wilson Toussile. "Clustering and variable selection for categorical multivariate data." Electron. J. Statist. 7 2344 - 2371, 2013.


Published: 2013
First available in Project Euclid: 19 September 2013

zbMATH: 1349.62259
MathSciNet: MR3108816
Digital Object Identifier: 10.1214/13-EJS844

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society


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