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Finite mixture regression: A sparse variable selection by model selection for clustering by Devijver, Emilie is a scholarly article available to read on EtoBox.

What is Finite mixture regression: A sparse variable selection by model selection for clustering about?

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.

Author

Devijver, Emilie

Published

2014

Language

EN