Improvement of mixed predictors in linear mixed models

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dc.authorid0000-0001-5632-001Xen_US
dc.authorid0000-0001-7085-7403en_US
dc.contributor.authorKuran, Özge
dc.contributor.authorÖzkale, M. Revan
dc.date.accessioned2024-03-06T10:52:41Z
dc.date.available2024-03-06T10:52:41Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.description.abstractIn this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean square error matrix criterion. Finally, numerical examples and a simulation study are done to illustrate the findings. In numerical examples, we took some arbitrary observations from the data as the prior information since we did not have historical data or additional information about the data sets. The results show that this case does the new estimator gain efficiency over the constituent estimators and provide accurate estimation and prediction of the data.en_US
dc.identifier.citationKuran, Ö. ve Özkale, M. R. (2021). Improvement of mixed predictors in linear mixed models. Journal of Applied Statistics, 48(5), 924-942.en_US
dc.identifier.doi10.1080/02664763.2020.1833182
dc.identifier.endpage942en_US
dc.identifier.issn0266-4763
dc.identifier.issue5en_US
dc.identifier.pmid35707446
dc.identifier.scopus2-s2.0-85092488296
dc.identifier.scopusqualityQ1
dc.identifier.startpage924en_US
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/02664763.2020.1833182
dc.identifier.urihttps://hdl.handle.net/11468/13520
dc.identifier.volume48en_US
dc.identifier.wosWOS:000579381800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorKuran, Özge
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofJournal of Applied Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLinear mixed modelen_US
dc.subjectLiu predictoren_US
dc.subjectMixed predictoren_US
dc.subjectMulticollinearityen_US
dc.subjectStochastic-restricted Liu predictoren_US
dc.titleImprovement of mixed predictors in linear mixed modelsen_US
dc.titleImprovement of mixed predictors in linear mixed models
dc.typeArticleen_US

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