CHOICE OF SMOOTHING PARAMETER FOR KERNEL TYPE RIDGE ESTIMATORS IN SEMIPARAMETRIC REGRESSION MODELS

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dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201;
dc.authorwosidYılmaz, Ersin/ABC-3391-2021
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidAydin, Dursun/O-7618-2014
dc.contributor.authorYilmaz, Ersin
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAydin, Dursun
dc.date.accessioned2024-08-04T20:57:22Z
dc.date.available2024-08-04T20:57:22Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman's approach based on kernel smoothing method. The most important factor in achieving this smoothing method is the selection of the smoothing parameter. In the literature, many selection criteria for comparing regression models have been produced. We will focus on six selection criterion improved version of Akaike information criterion (AIC(c)), generalized cross-validation (GCV), Mallows' C-p criterion, risk estimation using classical pilots (RECP), Bayes information criterion (BIC), and restricted maximum likelihood (REML). Real and simulated data sets are considered to illustrate the key ideas in the paper. Thus, suitable selection criterion are provided for optimum smoothing parameter selection.en_US
dc.identifier.endpage69en_US
dc.identifier.issn1645-6726
dc.identifier.issn2183-0371
dc.identifier.issue1en_US
dc.identifier.startpage47en_US
dc.identifier.urihttps://hdl.handle.net/11616/102576
dc.identifier.volume19en_US
dc.identifier.wosWOS:000635597300004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherInst Nacional Estatistica-Ineen_US
dc.relation.ispartofRevstat-Statistical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectsemiparametric modelen_US
dc.subjectkernel smoothingen_US
dc.subjectridge type estimatoren_US
dc.subjectsmoothing parameter generalized cross-validationen_US
dc.titleCHOICE OF SMOOTHING PARAMETER FOR KERNEL TYPE RIDGE ESTIMATORS IN SEMIPARAMETRIC REGRESSION MODELSen_US
dc.typeArticleen_US

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