A mixture model for dimension reduction

Jean-Luc Dortet-Bernadet and Laurent Gardes

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 21, 10768-10787

Abstract: The existence of a dimension reduction (DR) subspace is a common assumption in regression analysis when dealing with high-dimensional predictors. The estimation of such a DR subspace has received considerable attention in the past few years, the most popular method being undoubtedly the sliced inverse regression. In this paper, we propose a new estimation procedure of the DR subspace by assuming that the joint distribution of the predictor and the response variables is a finite mixture of distributions. The new method is compared through a simulation study to some classical methods.

Date: 2017
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DOI: 10.1080/03610926.2016.1248576

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