Kernel ridge prediction method in partially linear mixed measurement error model

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dc.authorid0000-0001-5632-001Xen_US
dc.authorid0000-0001-7283-9225en_US
dc.contributor.authorKuran, Özge
dc.contributor.authorYalaz, Seçil
dc.date.accessioned2023-04-07T10:52:27Z
dc.date.available2023-04-07T10:52:27Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.description.abstractIn this article, a new kernel prediction method by using ridge regression approach is suggested to combat multicollinearity and the impacts of its existence on various views of partially linear mixed measurement error model. We derive the necessary and sufficient condition for the superiority of the linear combinations of the predictors in the sense of the matrix mean square error criterion and give the selection of the ridge biasing parameter. The asymptotic normality condition is investigated and the unknown covariance matrix of measurement errors circumstance is handled. A real data analysis together with a Monte Carlo simulation study is made to assess endorsement of the kernel ridge prediction method.en_US
dc.identifier.citationKuran, Ö. ve Yalaz, S. (2022). Kernel ridge prediction method in partially linear mixed measurement error model. Communications in Statistics - Simulation and Computation, Early Accessen_US
dc.identifier.doi10.1080/03610918.2022.2075389
dc.identifier.endpage21en_US
dc.identifier.issn0361-0918
dc.identifier.issn1532-4141
dc.identifier.scopus2-s2.0-85130505728
dc.identifier.scopusqualityQ2
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/03610918.2022.2075389
dc.identifier.urihttps://hdl.handle.net/11468/11641
dc.identifier.volumeEarly Accessen_US
dc.identifier.wosWOS:000795724400001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKuran, Özge
dc.institutionauthorYalaz, Seçil
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofCommunications in Statistics - Simulation and Computation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAsymptotic normalityen_US
dc.subjectKernel ridge predictionen_US
dc.subjectMeasurement erroren_US
dc.subjectMulticollinearityen_US
dc.subjectPartially linear mixed modelen_US
dc.titleKernel ridge prediction method in partially linear mixed measurement error modelen_US
dc.titleKernel ridge prediction method in partially linear mixed measurement error model
dc.typeArticleen_US

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