On the extension of sliced average variance estimation to multivariate regression

被引：11

作者：

Yoo, Jae Keun ^{[1
]}

Lee, Keunbaik ^{[2
]}

Wu, Seongho ^{[3
]}

机构：

[1] Ewha Womans Univ, Dept Stat, Seoul 120750, South Korea

[2] Louisiana State Univ, Hlth Sci Ctr, Sch Publ Hlth, Biostat Program, New Orleans, LA 70112 USA

[3] Fed Natl Mortgage Assoc Fannie Mae, Washington, DC 20016 USA

来源：

STATISTICAL METHODS AND APPLICATIONS | 2010年 / 19卷 / 04期

关键词：

Double slicing; K-means clustering; Multivariatere gression; Pooled estimation; Sliced average variance estimation; SUFFICIENT DIMENSION REDUCTION; INVERSE REGRESSION;

D O I：

10.1007/s10260-010-0145-9

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：

Many sufficient dimension reduction methods for univariate regression have been extended to multivariate regression. Sliced average variance estimation (SAVE) has the potential to recover more reductive information and recent development enables us to test the dimension and predictor effects with distributions commonly used in the literature. In this paper, we aim to extend the functionality of the SAVE to multivariate regression. Toward the goal, we propose three new methods. Numerical studies and real data analysis demonstrate that the proposed methods perform well.

引用

页码：529 / 540

页数：12

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