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dc.contributor.authorÇetin, Meral
dc.contributor.authorErar, Aydin
dc.date.accessioned2025-01-09T20:14:26Z
dc.date.available2025-01-09T20:14:26Z
dc.date.issued2006
dc.identifier.issn0096-3003
dc.identifier.issn1873-5649
dc.identifier.urihttps://doi.org/10.1016/j.amc.2005.09.010
dc.identifier.urihttps://hdl.handle.net/20.500.14124/9061
dc.description.abstractIn linear regression analysis, outliers often have large influence in the variable selection process. The aim of this study is to select the subsets of independent variables, which explain dependent variables in the presence of outliers and possible departures from the normality assumption of the error distribution in robust regression analysis. We compared robust and classical variable selection. Here, as a classics selection criteria we used Cp, AICC and AICF which we proposed. Besides we used Andrews, Huber and Hampel M-estimators in computing of the robust variable selection criteria. (c) 2005 Elsevier Inc. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofApplied Mathematics and Computationen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectrobust variable selectionen_US
dc.subjectrobust regressionen_US
dc.subjectM-estimatorsen_US
dc.subjectMallows' Cp and Akaike criteriaen_US
dc.titleA simulation study on classic and robust variable selection in linear regressionen_US
dc.typearticleen_US
dc.departmentMimar Sinan Güzel Sanatlar Üniversitesien_US
dc.identifier.doi10.1016/j.amc.2005.09.010
dc.identifier.volume175en_US
dc.identifier.issue2en_US
dc.identifier.startpage1629en_US
dc.identifier.endpage1643en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.wosqualityQ2
dc.identifier.wosWOS:000237568000052
dc.indekslendigikaynakWeb of Scienceen_US
dc.snmzKA_20250105


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