A selective review of sufficient dimension reduction for multivariate response regression
Methodology
2022-02-03 v1 Computation
Machine Learning
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.
Keywords
Cite
@article{arxiv.2202.00876,
title = {A selective review of sufficient dimension reduction for multivariate response regression},
author = {Yuexiao Dong and Abdul-Nasah Soale and Michael D. Power},
journal= {arXiv preprint arXiv:2202.00876},
year = {2022}
}