A mixture model for dimension reduction

Jean-Luc Dortet-Bernadet (1) , Laurent Gardes (1)

1 IRMA - Institut de Recherche Mathématique Avancée

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. We propose in this paper 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.

Mots clés: Dimension reduction; Maximum likelihood estimates; Mixture of distributions; Sliced Inverse Regression

Domaines: Statistiques [math.ST]

Jean-Luc Dortet-Bernadet, Laurent Gardes. A mixture model for dimension reduction. 2016. ⟨hal-01077146v3⟩