Improved penalty strategies in linear regression models

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dc.authorscopusid55581057800
dc.authorscopusid57216190371
dc.authorscopusid57217110888
dc.contributor.authorYüzbaşı B.
dc.contributor.authorEjaz Ahmed S.
dc.contributor.authorGüngör M.
dc.date.accessioned2024-08-04T20:02:33Z
dc.date.available2024-08-04T20:02:33Z
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. © 2017, National Statistical Institute. All rights reserved.en_US
dc.description.sponsorshipTubitak-Bideb-2214/A; Natural Sciences and Engineering Research Council of Canada, NSERC; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada, CANRIMT, NSERC; Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, ExCELLSen_US
dc.description.sponsorshipThe authors thank the Associate Editor and two anonymous referees for their valuable comments that improved the paper. Bahadır Yüzbaşı was supported by The Scientific and Research Council of Turkey under grant Tubitak-Bideb-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.scopus2-s2.0-85018818289en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage251en_US
dc.identifier.urihttps://hdl.handle.net/11616/91760
dc.identifier.volume15en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherNational Statistical Instituteen_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.subjectAsymp-totic and Simulationen_US
dc.subjectFull Modelen_US
dc.subjectMulticollinearityen_US
dc.subjectPretest and Shrinkage Estimationen_US
dc.subjectSub-modelen_US
dc.titleImproved penalty strategies in linear regression modelsen_US
dc.typeArticleen_US

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