Regularization in statistics

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-dimens...

Full description

Saved in:
Bibliographic Details
Published in:Test (Madrid, Spain) Spain), 2006-09, Vol.15 (2), p.271-344
Main Authors: Bickel, Peter J., Li, Bo, Tsybakov, Alexandre B., van de Geer, Sara A., Yu, Bin, Valdés, Teófilo, Rivero, Carlos, Fan, Jianqing, van der Vaart, Aad
Format: Article
Language:English
Subjects:
Regularization - linear regression - nonparametric regression - boosting - covariance matrix - principal component - bootstrap - subsampling - model selection
Regularization methods
Statistics
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.[PUBLICATION ABSTRACT]
ISSN:1133-0686
1863-8260
DOI:10.1007/BF02607055