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dc.contributor.authorTutz, Gerharden
dc.contributor.authorUlbricht, Janen
dc.date.accessioned2006-10-16-
dc.date.accessioned2010-05-14T10:10:29Z-
dc.date.available2010-05-14T10:10:29Z-
dc.date.issued2006-
dc.identifier.pidoi:10.5282/ubm/epub.1854en
dc.identifier.piurn:nbn:de:bvb:19-epub-1854-7en
dc.identifier.urihttp://hdl.handle.net/10419/31101-
dc.description.abstractA new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors. As the elastic net, the method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. A boosted version of the penalized estimator, which is based on a new boosting method, allows to select variables. Real world data and simulations show that the method compares well to competing regularization techniques. In settings where the number of predictors is smaller than the number of observations it frequently performs better than competitors, in high dimensional settings prediction measures favor the elastic net while accuracy of estimation and stability of variable selection favors the newly proposed method.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x486en
dc.subject.ddc519en
dc.subject.keywordCorrelation based estimatoren
dc.subject.keywordBoostingen
dc.subject.keywordVariable selectionen
dc.subject.keywordElastic neten
dc.subject.keywordLassoen
dc.subject.keywordPenalizationen
dc.titlePenalized regression with correlation based penalty-
dc.type|aWorking Paperen
dc.identifier.ppn518770192en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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