A robust inverse regression estimator

被引：6

作者：

Ni, Liqiang ^{[1
]}

Cook, R. Dennis

机构：

[1] Univ Cent Florida, Dept Stat & Actuarial Sci, Orlando, FL 32816 USA

[2] Univ Minnesota, Sch Stat, St Paul, MN 55108 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2007年 / 77卷 / 03期

基金：

美国国家科学基金会;

关键词：

central subspace; inverse regression estimator; sufficient dimension reduction;

D O I：

10.1016/j.spl.2006.07.018

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：

A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410-428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. In this article, we propose a robust version of the IRE that only uses second moments of the predictor for estimation and inference, leading to better small sample results. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：343 / 349

页数：7

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