Optimal sufficient dimension reduction for the conditional mean in multivariate regression

被引：27
|
作者：
Yoo, Jae Keun
机构：
[1] Univ Louisville, Sch Publ & Informat Sci, Dept Bioinformat & Biostat, Louisville, KY 40292 USA
[2] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA
来源：
BIOMETRIKA | 2007年 / 94卷 / 01期
基金：
美国国家科学基金会;
关键词：
multivariate conditional mean; multivariate regression; predictor effect test; sufficient dimension reduction;
D O I：
10.1093/biomet/asm003
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The aim of this article is to develop optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression. The context is roughly the same as that of a related method by Cook & Setodji (2003), but the new method has several advantages. It is asymptotically optimal in the sense described herein and its test statistic for dimension always has a chi-squared distribution asymptotically under the null hypothesis. Additionally, the optimal method allows tests of predictor effects. A comparison of the two methods is provided.
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页码：231 / 242
页数：12
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