Liu estimator in logistic regression when the data are collinear

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dc.contributor.authorUrgan, Nurkut Nuray
dc.contributor.authorTez, Muejgan
dc.date.accessioned2024-04-24T17:37:37Z
dc.date.available2024-04-24T17:37:37Z
dc.date.issued2008
dc.departmentDicle Üniversitesien_US
dc.description20th International Conference/Euro Mini Conference on Continuous Optimization and Knowledge-Based Technologies (EurOPT 2008) -- MAY 20-23, 2008 -- Neringa, LITHUANIAen_US
dc.description.abstractThe logistic regression model is used to predict a binary response variable. Logistic regression using maximum likelihood estimation has gained widespread use but it is found that multicollinearity among the independent variables inflates the variance of this estimator. Previously, Ridge, Principal Component and Stein estimators were proposed instead of maximum likelihood estimator when the data are collinear. And in this study a Liu type estimator is proposed that will have smaller mean squared error than the maximum likelihood estimator. And Liu type estimator and several alternative estimators in logistic regression, such as Ridge, Stein, principal component, are compared under the mean squared error criterion.en_US
dc.description.sponsorshipEuropean Assoc Operat Res Soc,Inst Math & Informat,Vilnius Gediminas Tech Univ,Lithuanian Operat Res Soc,EURO WG Continuous Optimizat,German OR Soc,European Chapter Metaheurist,European Chapter Combinatorial Optimizat,IBM Europe,Kaunas Univ Technolen_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-9955-28-283-9
dc.identifier.scopus2-s2.0-84905471622
dc.identifier.scopusqualityN/A
dc.identifier.startpage323en_US
dc.identifier.urihttps://hdl.handle.net/11468/21069
dc.identifier.wosWOS:000258881100056
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherVilnius Gediminas Technical Univ Press, Technikaen_US
dc.relation.ispartof20th International Conference, Euro Mini Conference Continuous Optimization and Knowledge-Based Technologies, Europt'2008
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLogistic Regressionen_US
dc.subjectCollinearityen_US
dc.subjectMaximum Likelihood Estimator (Mle)en_US
dc.subjectRidge Logisticen_US
dc.subjectEstimatoren_US
dc.subjectStein Estimatoren_US
dc.subjectLiu Estimatoren_US
dc.titleLiu estimator in logistic regression when the data are collinearen_US
dc.titleLiu estimator in logistic regression when the data are collinear
dc.typeConference Objecten_US

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