K-means inverse regression

被引：57

作者：

Setodji, CM ^{[1
]}

Cook, RD

机构：

[1] RAND Corp, Santa Monica, CA 90407 USA

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

来源：

TECHNOMETRICS | 2004年 / 46卷 / 04期

基金：

美国国家科学基金会;

关键词：

central subspaces; dimension reduction; functional data analysis; K-means clustering; multivariate regression; regression graphics; sliced inverse regression;

D O I：

10.1198/004017004000000437

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：

Li suggested the method of sliced inverse regression for dimension reduction in regressions with a univariate response. In this article we extend that method to multivariate regressions by introducing a new way of performing the slicing. This method applies for any number of response variables and may be particularly useful at the outset of an analysis, before positing a multivariate model. The emphasis is on application; no new asymptotic theory is presented.

引用

页码：421 / 429

页数：9

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