A selective review of sufficient dimension reduction for multivariate response regression

被引：1

作者：

Dong, Yuexiao ^{[1
]}

Soale, Abdul-Nasah ^{[2
]}

Power, Michael D. ^{[1
]}

机构：

[1] Temple Univ, Dept Stat Operat & Data Sci, Philadelphia, PA 19122 USA

[2] Case Western Reserve Univ, Dept Math Appl Math & Stat, Cleveland, OH 44106 USA

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2023年 / 226卷

关键词：

Minimum average variance estimation; Partial least squares; Projective resampling; Sliced inverse regression; SLICED INVERSE REGRESSION; CENTRAL MEAN SUBSPACE; ESTIMATOR; MOMENT; MODELS;

D O I：

10.1016/j.jspi.2023.02.003

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 includes 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.(c) 2023 Elsevier B.V. All rights reserved.

引用

页码：63 / 70

页数：8

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