LASSO, Iterative Feature Selection and the Correlation Selector: Oracle Inequalities and Numerical Performances

dc.creatorAlquier, Pierre
dc.date2007-10-24
dc.date2008-11-25
dc.date.accessioned2026-07-07T10:20:22Z
dc.date.available2026-07-07T10:20:22Z
dc.descriptionWe propose a general family of algorithms for regression estimation with quadratic loss. Our algorithms are able to select relevant functions into a large dictionary. We prove that a lot of algorithms that have already been studied for this task (LASSO and Group LASSO, Dantzig selector, Iterative Feature Selection, among others) belong to our family, and exhibit another particular member of this family that we call Correlation Selector in this paper. Using general properties of our family of algorithm we prove oracle inequalities for IFS, for the LASSO and for the Correlation Selector, and compare numerical performances of these estimators on a toy example.
dc.identifierhttps://arxiv.org/abs/0710.4466
dc.identifierhttp://arxiv.org/abs/0710.4466
dc.identifierElectronic Journal of Statistics 2 (2008) pp. 1129-1152
dc.identifierdoi:10.1214/08-EJS299
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/174823
dc.subjectStatistics Theory
dc.subject62G08 (Primary), Secondary 62J07 (Secondary), 62G15, 68T05
dc.titleLASSO, Iterative Feature Selection and the Correlation Selector: Oracle Inequalities and Numerical Performances
dc.typetext

Files

Collections