Regularization in statistics

被引：131

作者：

Bickel, Peter J. ^{[1
]}

Li, Bo

机构：

[1] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA

[2] Tsinghua Univ, Sch Econ & Management, Beijing, Peoples R China

来源：

TEST | 2006年 / 15卷 / 02期

关键词：

regularization; linear regression; nonparametric regression; boosting; covariance matrix; principal component; bootstrap; subsampling; model selection;

D O I：

10.1007/BF02607055

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：

This paper is a selective review of the regularization methods scattered in statistics literature. We introduce a general conceptual approach to regularization and fit most existing methods into it. We have tried to focus on the importance of regularization when dealing with today's high-dimensional objects: data and models. A wide range of examples are discussed, including nonparametric regression, boosting, covariance matrix estimation, principal component estimation, subsampling.

引用

页码：271 / 303

页数：33

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