Improving prediction by means of a two parameter approach in linear mixed models

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dc.authorid0000-0003-3840-3107en_US
dc.contributor.authorKuran, Özge
dc.contributor.authorÖzbay, Nimet
dc.date.accessioned2022-12-01T08:40:50Z
dc.date.available2022-12-01T08:40:50Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.description.abstractIn this article, two parameter estimator and two parameter predictor are defined via the penalized log-likelihood approach in linear mixed models. The recommended approach is quite useful when there is a strong linear relationship among the variables of fixed effects design matrix. The necessary and sufficient condition for the superiority of the two parameter predictor over the best linear unbiased predictor of linear combinations of fixed and random effects in the sense of matrix mean square error criterion is examined. Additionally, to enhance the practical utility of the two parameter estimator and the two parameter predictor, we focus on the selection issue of two biasing parameters. Thus, 10 different methods for choosing the unknown biasing parameters are offered. Two real data sets are analysed to test the performance of our new two parameter approach. In addition, a comprehensive Monte Carlo simulation is performed.en_US
dc.identifier.citationKuran, Ö. ve Özbay, N (2021). Improving prediction by means of a two parameter approach in linear mixed models. Journal of Statistical Computation and Simulation, 91(18), 3721-3743.en_US
dc.identifier.doi10.1080/00949655.2021.1946540
dc.identifier.endpage3743en_US
dc.identifier.issn0094-9655
dc.identifier.issn563-5163
dc.identifier.issue18en_US
dc.identifier.scopus2-s2.0-85109298987
dc.identifier.scopusqualityQ2
dc.identifier.startpage3721en_US
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/00949655.2021.1946540
dc.identifier.urihttps://hdl.handle.net/11468/10919
dc.identifier.volume91en_US
dc.identifier.wosWOS:000668785200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKuran, Özge
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofJournal of Statistical Computation and Simulation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulticollinearityen_US
dc.subjectPenalized log-likelihood approachen_US
dc.subjectTwo parameter estimatoren_US
dc.subjectTwo parameter predictoren_US
dc.subjectMean square erroren_US
dc.titleImproving prediction by means of a two parameter approach in linear mixed modelsen_US
dc.titleImproving prediction by means of a two parameter approach in linear mixed models
dc.typeArticleen_US

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