Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19

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dc.contributor.authorKuran, Özge
dc.contributor.authorYalaz, Seçil
dc.date.accessioned2024-04-24T16:24:32Z
dc.date.available2024-04-24T16:24:32Z
dc.date.issued2023
dc.departmentDicle Üniversitesien_US
dc.description.abstractIn this article, we define mixed predictor and stochastic restricted ridge predictor of partially linear mixed measurement error models by taking advantage of Kernel approximation. Under matrix mean square error criterion, we make the comparison of the superiorities the linear combinations of the new defined predictors. Then we investigate the asymptotic normality characteristics and the situation of the unknown covariance matrix of measurement errors. Finally, the study is ended with a Monte Carlo simulation study and COVID-19 data application.en_US
dc.identifier.citationKuran, Ö. ve Yalaz, S. (2023). Kernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19. Journal of Applied Statistics, 1-25.
dc.identifier.doi10.1080/02664763.2023.2248418
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.scopus2-s2.0-85168265725
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1080/02664763.2023.2248418
dc.identifier.urihttps://hdl.handle.net/11468/16755
dc.identifier.wosWOS:001050281800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of Applied Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulticollinearityen_US
dc.subjectKernel Mixed Predictoren_US
dc.subjectKernel Stochastic Restricted Ridge Predictoren_US
dc.subjectAsymptotic Normalityen_US
dc.subjectPartially Linear Mixed Measurement Error Modelsen_US
dc.titleKernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19en_US
dc.titleKernel mixed and Kernel stochastic restricted ridge predictions in the partially linear mixed measurement error models: an application to COVID-19
dc.typeArticleen_US

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