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A mixture model for dimension reduction

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  • Jean-Luc Dortet-Bernadet
  • Laurent Gardes

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.

Suggested Citation

  • Jean-Luc Dortet-Bernadet & Laurent Gardes, 2017. "A mixture model for dimension reduction," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(21), pages 10768-10787, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:21:p:10768-10787
    DOI: 10.1080/03610926.2016.1248576
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