Unifying the prediction strategies of Theil-Goldberger and Kibria-Lukman within linear mixed models

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dc.authoridKuran, Ozge/0000-0001-5632-001X
dc.contributor.authorKuran, Ozge
dc.date.accessioned2025-02-22T14:08:57Z
dc.date.available2025-02-22T14:08:57Z
dc.date.issued2024
dc.departmentDicle Üniversitesien_US
dc.description.abstractLinear mixed models employ the best linear unbiased estimator and the best linear unbiased predictor to estimate the parameter vectors for fixed and random effects. However, due to the undesirable variance properties of the best linear unbiased estimator in the presence of multicollinearity, alternative estimators and predictors are preferred. The Theil-Goldberger's and the Kibria-Lukman's prediction approaches are commonly used for prediction under multicollinearity in linear mixed models. To address the issue of multicollinearity, this article introduces the mixed Kibria-Lukman estimator and predictor by combining these prediction approaches. To assess their effectiveness, the proposed mixed Kibria-Lukman estimator/predictor is compared with other estimators/predictors, including the best linear unbiased estimator/the best linear unbiased predictor and mixed estimators/predictors, using the matrix mean square error criterion. Furthermore, the performance of the newly defined prediction approach is demonstrated through the analysis of greenhouse gases data and a Monte-Carlo simulation study.en_US
dc.identifier.doi10.1080/03610926.2024.2369309
dc.identifier.issn0361-0926
dc.identifier.issn1532-415X
dc.identifier.scopus2-s2.0-85198350418en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1080/03610926.2024.2369309
dc.identifier.urihttps://hdl.handle.net/11468/29728
dc.identifier.wosWOS:001270023700001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKuran, Ozge
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofCommunications in Statistics-Theory and Methodsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250222
dc.subjectKibria-Lukman predictoren_US
dc.subjectlinear mixed modelen_US
dc.subjectmean square erroren_US
dc.subjectmixed predictoren_US
dc.subjectmulticollinearityen_US
dc.titleUnifying the prediction strategies of Theil-Goldberger and Kibria-Lukman within linear mixed modelsen_US
dc.typeArticleen_US

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