Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

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dc.authoridArashi, Mohammad/0000-0002-5881-9241
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridArashi, Mohammad/0000-0002-5881-9241
dc.authorwosidArashi, Mohammad/AAO-4453-2021
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidArashi, Mohammad/ABD-3395-2020
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorArashi, Mohammad
dc.contributor.authorAhmed, S. Ejaz
dc.date.accessioned2024-08-04T20:47:04Z
dc.date.available2024-08-04T20:47:04Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.en_US
dc.description.sponsorshipIran National Science Foundation (INSF) [97019472]; Natural Sciences and the Engineering Research Council of Canada (NSERC)en_US
dc.description.sponsorshipWe would like to thank two anonymous reviewers for constructive comments that significantly improved the presentation of the paper. Mohammad Arashi's work is based on the research supported in part by the Iran National Science Foundation (INSF) (grant number 97019472). Prof. S. Ejaz Ahmed is supported by the Natural Sciences and the Engineering Research Council of Canada (NSERC).en_US
dc.identifier.doi10.1111/insr.12351
dc.identifier.endpage251en_US
dc.identifier.issn0306-7734
dc.identifier.issn1751-5823
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85077843643en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage229en_US
dc.identifier.urihttps://doi.org/10.1111/insr.12351
dc.identifier.urihttps://hdl.handle.net/11616/99142
dc.identifier.volume88en_US
dc.identifier.wosWOS:000506063400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Statistical Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGeneralised ridge regressionen_US
dc.subjectlow-dimensional and high-dimensional dataen_US
dc.subjectmulticollinearityen_US
dc.subjectpenalty estimationen_US
dc.subjectshrinkage estimationen_US
dc.titleShrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regimeen_US
dc.typeArticleen_US

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