On the stochastic restricted Liu estimator in logistic regression model

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dc.contributor.authorLi, Yong
dc.contributor.authorAsar, Yasin
dc.contributor.authorWu, Jibo
dc.date.accessioned2024-02-23T14:17:12Z
dc.date.available2024-02-23T14:17:12Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractIn this paper, we study the effects of near-singularity which is known as multicollinearity in the binary logistic regression. Furthermore, we also assume the presence of stochastic non-sample linear restrictions. The well-known logistic Liu estimator is combined with the stochastic linear restrictions in order to propose a new method, namely, the stochastic restricted Liu estimation. Theoretical comparisons between the usual maximum likelihood estimator, Liu estimator, stochastic restricted maximum-likelihood estimator and the new stochastic restricted Liu estimator are derived using matrix mean-squared errors of the estimators. A Monte Carlo simulation experiment is designed to evaluate the performances of the listed estimators in terms of mean-squared error and mean absolute error criteria. Artificial data are used to show how to interpret the theorems. According to the results of the simulation, the new method beats the other estimators when the data matrix has the problem of collinearity along with the stochastic restrictions.en_US
dc.description.sponsorshipScientific Technological Research Program of Chongqing Municipal Education Commission [KJQN201901347]; Natural Science Foundation of Chongqing [cstc2019jcyj-msxmX0379]en_US
dc.description.sponsorshipThis work was sponsored by the Scientific Technological Research Program of Chongqing Municipal Education Commission [grant no. KJQN201901347] and the Natural Science Foundation of Chongqing [grant no. cstc2019jcyj-msxmX0379].en_US
dc.identifier.doi10.1080/00949655.2020.1790561
dc.identifier.endpage2788en_US
dc.identifier.issn0094-9655
dc.identifier.issn1563-5163
dc.identifier.issue15en_US
dc.identifier.scopus2-s2.0-85087823189en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2766en_US
dc.identifier.urihttps://doi.org/10.1080/00949655.2020.1790561
dc.identifier.urihttps://hdl.handle.net/20.500.12452/12991
dc.identifier.volume90en_US
dc.identifier.wosWOS:000547055900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal Of Statistical Computation And Simulationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulticollinearityen_US
dc.subjectStochastic Restricted Liu Estimatoren_US
dc.subjectLiu Logistic Estimatoren_US
dc.subjectLogistic Regression Modelen_US
dc.titleOn the stochastic restricted Liu estimator in logistic regression modelen_US
dc.typeArticleen_US

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