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	Regularization in statistics
	Bickel, Peter J. ; Li, Bo
	2010-05-11 ; 2010-05-11
关键词	regularization linear regression nonparametric regression boosting covariance matrix principal component bootstrap subsampling model selection GENERALIZED CROSS-VALIDATION NONCONCAVE PENALIZED LIKELIHOOD VARIABLE SELECTION LONGITUDINAL DATA COVARIANCE MATRICES MODEL SELECTION ASYMPTOTIC OPTIMALITY PRINCIPAL COMPONENTS RIDGE-REGRESSION DENSITY-FUNCTION Statistics & Probability
中文摘要	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.
语种	英语 ; 英语
出版者	SOCIEDAD ESTADISTICA INVESTIGACION OPERATIVA ; MADRID ; HORTALEZA 104, 2 IZDA, 28004 MADRID, SPAIN
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/26121]
专题	清华大学
推荐引用方式 GB/T 7714	Bickel, Peter J.,Li, Bo. Regularization in statistics[J],2010, 2010.
APA	Bickel, Peter J.,&Li, Bo.(2010).Regularization in statistics..
MLA	Bickel, Peter J.,et al."Regularization in statistics".(2010).

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