A class of shrinkage estimators in linear regression

被引：1

作者：

Blaker, H ^{[1
]}

机构：

[1] Univ Oslo, Dept Math, N-0316 Oslo, Norway

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 1999年 / 27卷 / 01期

关键词：

principal components; orthogonal decomposition; Stein estimation; predictive risk; multicollinearity;

D O I：

10.2307/3315502

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 consider the problem of using shrinkage estimators that shrink towards subspaces in linear regression, in particular subspaces spanned by principal components. This is especially important when multicollinearity is present and the number of predictors is not small compared to the sample size. New theoretical results about Stein estimation are used to get estimators with lower theoretical risk than standard Stein estimators used by Oman (1991). Application of the techniques to real data is largely successful.

引用

页码：207 / 220

页数：14

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