. IMPROVED PENALTY STRATEGIES in LINEAR REGRESSION MODELS

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dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridGüngör, Mehmet/0000-0001-6869-4043
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidGüngör, Mehmet/ABI-7228-2020
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAhmed, S. Ejaz
dc.contributor.authorGungor, Mehmet
dc.date.accessioned2024-08-04T20:56:14Z
dc.date.available2024-08-04T20:56:14Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractWe suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators. Further, a Monte Carlo simulation study is conducted to assess the relative performance of the listed estimators. Also, we numerically compare their performance with Lasso, adaptive Lasso and SCAD strategies. Finally, a real data example is presented to illustrate the usefulness of the suggested methods.en_US
dc.description.sponsorshipScientific and Research Council of Turkey [TubitakBideb-2214/A]; Brock University in Canada; Natural Sciences and the Engineering Research Council of Canada (NSERC)en_US
dc.description.sponsorshipThe authors thank the Associate Editor and two anonymous referees for their valuable comments that improved the paper. Bahadir Yuzbasi was supported by The Scientific and Research Council of Turkey under grant TubitakBideb-2214/A during this study at Brock University in Canada, and S. Ejaz Ahmed is supported by the Natural Sciences and the Engineering Research Council of Canada (NSERC).en_US
dc.identifier.endpage276en_US
dc.identifier.issn1645-6726
dc.identifier.issue2en_US
dc.identifier.startpage251en_US
dc.identifier.urihttps://hdl.handle.net/11616/102152
dc.identifier.volume15en_US
dc.identifier.wosWOS:000400670400005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherInst Nacional Estatistica-Ineen_US
dc.relation.ispartofRevstat-Statistical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSub-modelen_US
dc.subjectFull Modelen_US
dc.subjectPretest and Shrinkage Estimationen_US
dc.subjectMulticollinearityen_US
dc.subjectAsymp-totic and Simulationen_US
dc.title. IMPROVED PENALTY STRATEGIES in LINEAR REGRESSION MODELSen_US
dc.typeArticleen_US

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