Likelihood-Based Sufficient Dimension Reduction

被引：116

作者：

Cook, R. Dennis ^{[1
]}

Forzani, Liliana ^{[2
,3
]}

机构：

[1] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA

[2] Consejo Nacl Invest Cient & Tecn, Inst Matemat Aplicada Litoral, RA-3000 Santa Fe, Argentina

[3] Univ Nacl Litoral, Fac Ingn Quim, RA-3000 Santa Fe, Argentina

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2009年 / 104卷 / 485期

基金：

美国国家科学基金会;

关键词：

Central subspace; Directional regression; Grassmann manifolds; Sliced average variance estimations; Sliced inverse regression; SLICED INVERSE REGRESSION;

D O I：

10.1198/jasa.2009.0106

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：

We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.

引用

页码：197 / 208

页数：12

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