Rank-based Liu regression

Basit Öğe Kaydı
dc.authoridArashi, Mohammad/0000-0002-5881-9241
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridNorouzirad, Mina/0000-0003-0311-6888
dc.authoridArashi, Mohammad/0000-0002-5881-9241
dc.authorwosidArashi, Mohammad/AAO-4453-2021
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidNorouzirad, Mina/A-1886-2019
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.authorwosidArashi, Mohammad/ABD-3395-2020
dc.contributor.authorArashi, Mohammad
dc.contributor.authorNorouzirad, Mina
dc.contributor.authorAhmed, S. Ejaz
dc.contributor.authorYuzbasi, Bahadir
dc.date.accessioned2024-08-04T20:44:24Z
dc.date.available2024-08-04T20:44:24Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDue to the complicated mathematical and nonlinear nature of ridge regression estimator, Liu (Linear-Unified) estimator has been received much attention as a useful method to overcome the weakness of the least square estimator, in the presence of multicollinearity. In situations where in the linear model, errors are far away from normal or the data contain some outliers, the construction of Liu estimator can be revisited using a rank-based score test, in the line of robust regression. In this paper, we define the Liu-type rank-based and restricted Liu-type rank-based estimators when a sub-space restriction on the parameter of interest holds. Accordingly, some improved estimators are defined and their asymptotic distributional properties are investigated. The conditions of superiority of the proposed estimators for the biasing parameter are given. Some numerical computations support the findings of the paper.en_US
dc.description.sponsorshipNational Research Foundation of South Africa [109214]; Natural Sciences and the Engineering Research Council of Canada (NSERC)en_US
dc.description.sponsorshipWe would like to thank two anonymous referees for their valuable and constructive comments which significantly improved the presentation of the paper and led to put many details. First author Mohammad Arashi's work is based on the research supported in part by the National Research Foundation of South Africa (Grant NO. 109214). Third author S. Ejaz Ahmed is supported by the Natural Sciences and the Engineering Research Council of Canada (NSERC).en_US
dc.identifier.doi10.1007/s00180-018-0809-8
dc.identifier.endpage1561en_US
dc.identifier.issn0943-4062
dc.identifier.issn1613-9658
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85044579502en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1525en_US
dc.identifier.urihttps://doi.org/10.1007/s00180-018-0809-8
dc.identifier.urihttps://hdl.handle.net/11616/98217
dc.identifier.volume33en_US
dc.identifier.wosWOS:000436998200020en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofComputational Statisticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLiu estimatoren_US
dc.subjectMulticollinearityen_US
dc.subjectPreliminary testen_US
dc.subjectRank-based estimatoren_US
dc.subjectRidge regressionen_US
dc.subjectShrinkage estimatoren_US
dc.titleRank-based Liu regressionen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu