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Journal Articles Electronic Journal of Statistics Year : 2015

Finite mixture regression: a sparse variable selection by model selection for clustering.

Abstract

We consider a finite mixture of Gaussian regression model for high- dimensional data, where the number of covariates may be much larger than the sample size. We propose to estimate the unknown conditional mixture density by a maximum likelihood estimator, restricted on relevant variables selected by an 1-penalized maximum likelihood estimator. We get an oracle inequality satisfied by this estimator with a Jensen-Kullback-Leibler type loss. Our oracle inequality is deduced from a general model selection theorem for maximum likelihood estimators with a random model collection. We can derive the penalty shape of the criterion, which depends on the complexity of the random model collection.
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hal-01060079 , version 1 (03-09-2014)

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Emilie Devijver. Finite mixture regression: a sparse variable selection by model selection for clustering.. Electronic Journal of Statistics , 2015. ⟨hal-01060079⟩
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