Penalized regression via the restricted bridge estimator

Basit Öğe Kaydı
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridArashi, Mohammad/0000-0002-5881-9241
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidArashi, Mohammad/ABD-3395-2020
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorArashi, Mohammad
dc.contributor.authorAkdeniz, Fikri
dc.date.accessioned2024-08-04T20:50:18Z
dc.date.available2024-08-04T20:50:18Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis article is concerned with the bridge regression, which is a special family in penalized regression with penalty function Sigma(p)(j=1) |beta(j) (q) with q > 0, in a linear model with linear restrictions. The proposed restricted bridge (RBRIDGE) estimator simultaneously estimates parameters and selects important variables when a piece of prior information about parameters are available in either low-dimensional or high-dimensional case. Using local quadratic approximation, we approximate the penalty term around a local initial values vector. The RBRIDGE estimator enjoys a closed-form expression that can be solved when q > 0. Special cases of our proposal are the restricted LASSO (q = 1), restricted RIDGE (q = 2), and restricted Elastic Net (1 < q < 2) estimators. We provide some theoretical properties of the RBRIDGE estimator for the low-dimensional case, whereas the computational aspects are given for both low- and high-dimensional cases. An extensive Monte Carlo simulation study is conducted based on different prior pieces of information. The performance of the RBRIDGE estimator is compared with some competitive penalty estimators and the ORACLE. We also consider four real-data examples analysis for comparison sake. The numerical results show that the suggested RBRIDGE estimator outperforms outstandingly when the prior is true or near exact.en_US
dc.description.sponsorshipInonu University Scientific Researches Unit [SUA-2019-1629]en_US
dc.description.sponsorshipThe authors thank the editor and reviewer for their detailed reading of the manuscript and their valuable comments and suggestions that led to a considerable improvement of the paper. Prof. Yuzba was supported by Inonu University Scientific Researches Unit with the project number SUA-2019-1629 during his visit to the University of British Columbia, Vancouver, Canada.en_US
dc.identifier.doi10.1007/s00500-021-05763-9
dc.identifier.endpage8416en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85107873517en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage8401en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-021-05763-9
dc.identifier.urihttps://hdl.handle.net/11616/99985
dc.identifier.volume25en_US
dc.identifier.wosWOS:000641235700004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBridge regressionen_US
dc.subjectRestricted estimationen_US
dc.subjectMachine learningen_US
dc.subjectQuadratic approximationen_US
dc.subjectNewton-Raphsonen_US
dc.subjectVariable selectionen_US
dc.subjectMulticollinearityen_US
dc.titlePenalized regression via the restricted bridge estimatoren_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu