Tuning Parameter Estimation in Penalized Least Squares Methodology

Androulakis, E; Koukouvinos, C; Mylona, K

dc.contributor.author	Androulakis, E	en
dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Mylona, K	en
dc.date.accessioned	2014-03-01T02:07:08Z
dc.date.available	2014-03-01T02:07:08Z
dc.date.issued	2011	en
dc.identifier.issn	0361-0918	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29521
dc.subject	Error estimation	en
dc.subject	Generalized cross validation	en
dc.subject	Penalized least squares	en
dc.subject	Tuning parameter	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	SELECTION	en
dc.subject.other	LASSO	en
dc.subject.other	REGRESSION	en
dc.title	Tuning Parameter Estimation in Penalized Least Squares Methodology	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	The efficiency of the penalized methods (Fan and Li, 2001) depends strongly on a tuning parameter due to the fact that it controls the extent of penalization. Therefore, it is important to select it appropriately. In general, tuning parameters are chosen by data-driven approaches, such as the commonly used generalized cross validation. In this article, we propose an alternative method for the derivation of the tuning parameter selector in penalized least squares framework, which can lead to an ameliorated estimate. Simulation studies are presented to support theoretical findings and a comparison of the Type I and Type II error rates, considering the L1, the hard thresholding and the Smoothly Clipped Absolute Deviation penalty functions, is performed. The results are given in tables and discussion follows.	en
heal.publisher	TAYLOR & FRANCIS INC	en
heal.journalName	COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION	en
dc.identifier.isi	ISI:000291275900008	en
dc.identifier.volume	40	en
dc.identifier.issue	9	en
dc.identifier.spage	1444	en
dc.identifier.epage	1457	en