A selective review of sufficient dimension reduction for multivariate response regression

2022/02/02 by Yuexiao Dong, Dong, Yuexiao, Abdul‐Nasah Soale +4
Computer Science · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (stat.ML) #Methodology (stat.ME) #stat.CO #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.2202.00876

arxiv created 2022/02/02 · openalex publication_date 2022/02/02 · arxiv updated 2022/02/03 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28

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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