Kernel Liu prediction approach in partially linear mixed measurement error models

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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.accessioned2024-04-17T11:12:16Z
dc.date.available2024-04-17T11:12:16Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.description.abstractIn this paper, we put forward ‘kernel Liu prediction approach’ instead of ‘kernel prediction approach’ under multicollinearity case in partially linear mixed measurement error model. We obtain 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 Liu biasing parameter via the Conceptual Prediction ((Formula presented.)) criterion. The asymptotic normality condition is examined and the unknown covariance matrix of measurement errors circumstance is derived. We study a numerical example together with a Monte Carlo simulation study to evaluate the performance of the kernel Liu prediction approach at the end of this paper.en_US
dc.identifier.citationKuran, Ö. ve Yalaz, S. (2022). Kernel Liu prediction approach in partially linear mixed measurement error models, Statistics, 56(6), 1385-1408.en_US
dc.identifier.doi10.1080/02331888.2022.2152816
dc.identifier.endpage1408en_US
dc.identifier.issn0233-1888
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85143358400
dc.identifier.scopusqualityQ4
dc.identifier.startpage1385en_US
dc.identifier.urihttps://www.tandfonline.com/doi/epdf/10.1080/02331888.2022.2152816?needAccess=true
dc.identifier.urihttps://hdl.handle.net/11468/13904
dc.identifier.volume56en_US
dc.identifier.wosWOS:000893581300001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKuran, Özge
dc.institutionauthorYalaz, Seçil
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofStatistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAsymptotic normalityen_US
dc.subjectKernel Liu predictionen_US
dc.subjectMeasurement erroren_US
dc.subjectMulticollinearityen_US
dc.subjectPartially linear mixed modelen_US
dc.titleKernel Liu prediction approach in partially linear mixed measurement error modelsen_US
dc.titleKernel Liu prediction approach in partially linear mixed measurement error models
dc.typeArticleen_US

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