A variable selection proposal for multiple linear regression analysis

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dc.contributor.authorSteel S.J.
dc.contributor.authorUys D.W.
dc.date.accessioned2012-04-12T08:33:54Z
dc.date.available2012-04-12T08:33:54Z
dc.date.issued2011
dc.description.abstractVariable selection in multiple linear regression models is considered. It is shown that for the special case of orthogonal predictor variables, an adaptive pre-test-type procedure proposed by Venter and Steel [Simultaneous selection and estimation for the some zeros family of normal models, J. Statist. Comput. Simul. 45 (1993), pp. 129-146] is almost equivalent to least angle regression, proposed by Efron et al. [Least angle regression, Ann. Stat. 32 (2004), pp. 407-499]. A new adaptive pre-test-type procedure is proposed, which extends the procedure of Venter and Steel to the general non-orthogonal case in a multiple linear regression analysis. This new procedure is based on a likelihood ratio test where the critical value is determined data-dependently. A practical illustration and results from a simulation study are presented. © 2011 Copyright Taylor and Francis Group, LLC.
dc.identifier.citationJournal of Statistical Computation and Simulation
dc.identifier.citation81
dc.identifier.citation12
dc.identifier.citation2095
dc.identifier.citation2105
dc.identifier.issn949655
dc.identifier.other10.1080/00949655.2010.518569
dc.identifier.urihttp://hdl.handle.net/10019.1/20715
dc.subjectLARS
dc.subjectlasso
dc.subjectlimited translation
dc.subjectpre-test selection
dc.subjectunbiased risk estimation
dc.titleA variable selection proposal for multiple linear regression analysis
dc.typeArticle
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