Liu-Type Logistic Estimators with Optimal Shrinkage Parameter

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dc.contributor.authorAsar, Yasin
dc.date.accessioned2024-02-23T14:48:46Z
dc.date.available2024-02-23T14:48:46Z
dc.date.issued2016
dc.departmentNEÜen_US
dc.description.abstractMulticollinearity in logistic regression affects the variance of the maximum likelihood estimator negatively. In this study, Liu-type estimators are used to reduce the variance and overcome the multicollinearity by applying some existing ridge regression estimators to the case of logistic regression model. A Monte Carlo simulation is given to evaluate the performances of these estimators when the optimal shrinkage parameter is used in the Liutype estimators, along with an application of real case data.en_US
dc.identifier.endpage751en_US
dc.identifier.issn1538-9472
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-84975263295en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage738en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12452/17826
dc.identifier.volume15en_US
dc.identifier.wosWOS:000411568100035en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWayne State Univ Pressen_US
dc.relation.ispartofJournal Of Modern Applied Statistical Methodsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLogistic Regressionen_US
dc.subjectMulticollinearityen_US
dc.subjectMaximum Likelihooden_US
dc.subjectMseen_US
dc.subjectLiu-Type Estimatoren_US
dc.titleLiu-Type Logistic Estimators with Optimal Shrinkage Parameteren_US
dc.typeArticleen_US

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