On a new class of sufficient dimension reduction estimators

被引：1
|
作者：
Dong, Yuexiao [1 ]
Zhang, Yongxu [1 ]
机构：
[1] Temple Univ, Dept Stat Sci, Philadelphia, PA 19122 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2018年 / 139卷
关键词：
Linear conditional mean; Ordinary least squares; Sliced inverse regression; SLICED INVERSE REGRESSION;
D O I：
10.1016/j.spl.2018.03.019
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：
OLS and SIR are two popular sufficient dimension reduction estimators. OLS can recover at most one direction, and SIR shares this limitation when the response is binary. To address such limitation, we propose slicing-assisted OLS and slicing-assisted SIR. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：90 / 94
页数：5
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