K-means inverse regression

被引：53
|
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
收藏
页码：421 / 429
页数：9
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