Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model

Emilie Devijver

Journal of Multivariate Analysis, 2017, vol. 157, issue C, 1-13

Abstract: We study a dimensionality reduction technique for finite mixtures of high-dimensional multivariate response regression models. Both the dimension of the response and the number of predictors are allowed to exceed the sample size. We consider predictor selection and rank reduction to obtain lower-dimensional approximations. A class of estimators with a fast rate of convergence is introduced. We apply this result to a specific procedure, introduced in Devijver (in press), where the relevant predictors are selected by the Group-Lasso.

Keywords: Finite mixture regression; Low rank estimation; Non-asymptotic penalized criterion; Oracle inequality; Variable selection (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.jmva.2017.02.006

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