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	Shrinkage tuning parameter selection with a diverging number of parameters
	Wang, Hansheng ; Li, Bo ; Leng, Chenlei
	2010-10-12 ; 2010-10-12
关键词	Bayesian information criterion Diverging number of parameters Lasso Smoothly clipped absolute deviation NONCONCAVE PENALIZED LIKELIHOOD LINEAR-MODEL SELECTION ORACLE PROPERTIES ADAPTIVE LASSO BRIDGE Statistics & Probability
中文摘要	Contemporary statistical research frequently deals with problems involving a diverging number of parameters. For those problems, various shrinkage methods (e.g. the lasso and smoothly clipped absolute deviation) are found to be particularly useful for variable selection. Nevertheless, the desirable performances of those shrinkage methods heavily hinge on an appropriate selection of the tuning parameters. With a fixed predictor dimension, Wang and co-worker have demonstrated that the tuning parameters selected by a Bayesian information criterion type criterion can identify the true model consistently. In this work, similar results are further extended to the situation with a diverging number of parameters for both unpenalized and penalized estimators. Consequently, our theoretical results further enlarge not only the scope of applicabilityation criterion type criteria but also that of those shrinkage estimation methods.
语种	英语 ; 英语
出版者	WILEY-BLACKWELL PUBLISHING, INC ; MALDEN ; COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/78695]
专题	清华大学
推荐引用方式 GB/T 7714	Wang, Hansheng,Li, Bo,Leng, Chenlei. Shrinkage tuning parameter selection with a diverging number of parameters[J],2010, 2010.
APA	Wang, Hansheng,Li, Bo,&Leng, Chenlei.(2010).Shrinkage tuning parameter selection with a diverging number of parameters..
MLA	Wang, Hansheng,et al."Shrinkage tuning parameter selection with a diverging number of parameters".(2010).

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