Related papers: A selective review of sufficient dimension reduction for multivariate response regression

A selective review of sufficient dimension reduction for multivariate response regression

URL: http://arxiv.org/abs/2202.00876v1
Date: Wed, 2 Feb 2022 04:53:09 GMT
Title: A selective review of sufficient dimension reduction for multivariate response regression
Authors: Yuexiao Dong, Abdul-Nasah Soale, Michael D. Power
Abstract summary: A wide range of SDR methods are characterized as inverse regression SDR estimators or forward regression SDR estimators. Ordinary least squares, partial least squares, and semiparametric SDR estimators are discussed as estimators from the forward regression family.
Score: 2.492300648514129
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression SDR estimators or forward regression SDR estimators. The inverse regression family include pooled marginal estimators, projective resampling estimators, and distance-based estimators. Ordinary least squares, partial least squares, and semiparametric SDR estimators, on the other hand, are discussed as estimators from the forward regression family.

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