LAD, LASSO and Related Strategies in Regression Models

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dc.authoridArashi, Mohammad/0000-0002-5881-9241
dc.authoridNorouzirad, Mina/0000-0003-0311-6888
dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridArashi, Mohammad/0000-0002-5881-9241;
dc.authorwosidArashi, Mohammad/ABD-3395-2020
dc.authorwosidNorouzirad, Mina/A-1886-2019
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidArashi, Mohammad/AAO-4453-2021
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAhmed, Syed Ejaz
dc.contributor.authorArashi, Mohammad
dc.contributor.authorNorouzirad, Mina
dc.date.accessioned2024-08-04T20:46:01Z
dc.date.available2024-08-04T20:46:01Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description13th International Conference on Management Science and Engineering Management (ICMSEM) -- AUG 05-08, 2019 -- Brock Univ, St. Catharines, CANADAen_US
dc.description.abstractIn the context of linear regression models, it is well-known that the ordinary least squares estimator is very sensitive to outliers whereas the least absolute deviations (LAD) is an alternative method to estimate the known regression coefficients. Selecting significant variables is very important; however, by choosing these variables some information may be sacrificed. To prevent this, in our proposal, we can combine the full model estimates toward the candidate sub-model, resulting in improved estimators in risk sense. In this article, we consider shrinkage estimators in a sparse linear regression model and study their relative asymptotic properties. Advantages of the proposed estimators over the usual LAD estimator are demonstrated through a Monte Carlo simulation as well as a real data example.en_US
dc.description.sponsorshipInt Soc Management Sci & Engn Management,Sichuan Univen_US
dc.description.sponsorshipNational Research Foundation of South Africa [IFR170227223754, 109214]; Natural Sciences and the Engineering Research Council of Canada (NSERC)en_US
dc.description.sponsorshipWe would like to thank anonymous reviewers for constructive comments which significantly improved the presentation of the paper. M. Arashi's research is supported in part by the National Research Foundation of South Africa (ref. IFR170227223754 grant number 109214). Prof. S. Ejaz Ahmed is supported by the Natural Sciences and the Engineering Research Council of Canada (NSERC).en_US
dc.identifier.doi10.1007/978-3-030-21248-3_32
dc.identifier.endpage444en_US
dc.identifier.isbn978-3-030-21248-3
dc.identifier.isbn978-3-030-21247-6
dc.identifier.issn2194-5357
dc.identifier.issn2194-5365
dc.identifier.scopus2-s2.0-85068203410en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage429en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-21248-3_32
dc.identifier.urihttps://hdl.handle.net/11616/98849
dc.identifier.volume1001en_US
dc.identifier.wosWOS:000587663000032en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofProceedings of The Thirteenth International Conference on Management Science and Engineering Management, Vol 1en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLAD estimatoren_US
dc.subjectLAD-LASSO estimatoren_US
dc.subjectOutliersen_US
dc.subjectSoft and hard thresh-holdingsen_US
dc.titleLAD, LASSO and Related Strategies in Regression Modelsen_US
dc.typeConference Objecten_US

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