A COMPARATIVE STUDY ON THE PERFORMANCE OF FREQUENTIST AND BAYESIAN ESTIMATION METHODS UNDER SEPARATION IN LOGISTIC REGRESSION

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dc.authoridAltinisik, Yasin/0000-0001-9375-2276
dc.contributor.authorAltinisik, Yasin
dc.date.accessioned2025-03-23T19:26:37Z
dc.date.available2025-03-23T19:26:37Z
dc.date.issued2020
dc.departmentSinop Üniversitesi
dc.description.abstractSeparation is one of the most commonly encountered estimation problems in the context of logistic regression, which often occurs with small and medium sample sizes. The method of maximum likelihood (MLE; [8]) provides spuriously high parameter estimates and their standard errors under separation in logistic regression. Many researchers in social sciences utilize simple but ad-hoc solutions to overcome this issue, such as doing nothing strategy, removing variable(s) from the model, and combining the levels of the categorical variable in the data causing separation etc. The limitations of these basic solutions have motivated researchers to use more appropriate and innovative estimation techniques to deal with the problem. However, the performance and comparison of these techniques have not been fully investigated yet. The main goal of this paper is to close this research gap by comparing the performance of frequentist and Bayesian estimation methods for coping with separation. A simulation study is performed to investigate the performance of asymptotic, bootstrap-based, and Bayesian estimation techniques with respect to bias, precision, and accuracy measures under separation. In line with the simulation study, a real-data example is used to illustrate how to utilize these methods to solve separation in logistic regression.
dc.identifier.doi10.31801/cfsuasmas.614492
dc.identifier.endpage109
dc.identifier.issn1303-5991
dc.identifier.issue2
dc.identifier.scopusqualityN/A
dc.identifier.startpage89
dc.identifier.trdizinid439958
dc.identifier.urihttps://doi.org/10.31801/cfsuasmas.614492
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/439958
dc.identifier.urihttps://hdl.handle.net/11486/4740
dc.identifier.volume69
dc.identifier.wosWOS:000545434200008
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorAltinisik, Yasin
dc.language.isoen
dc.publisherAnkara Univ, Fac Sci
dc.relation.ispartofCommunications Faculty of Sciences University of Ankara-Series A1 Mathematics and Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectLogistic regression
dc.subjectseparation problem
dc.subjectfrequentist and Bayesian estimation
dc.subjectbias
dc.subjectprecision
dc.subjectaccuracy measures
dc.titleA COMPARATIVE STUDY ON THE PERFORMANCE OF FREQUENTIST AND BAYESIAN ESTIMATION METHODS UNDER SEPARATION IN LOGISTIC REGRESSION
dc.typeArticle

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