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dc.contributor.authorAtalik, G.
dc.contributor.authorSenturk, S.
dc.date.accessioned2019-09-01T13:04:22Z
dc.date.available2019-09-01T13:04:22Z
dc.date.issued2018
dc.identifier.issn1735-0654
dc.identifier.urihttps://hdl.handle.net/20.500.12450/922
dc.descriptionWOS: 000425854000005en_US
dc.description.abstractLogistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situations where the classical logistic regression assumptions' are not satisfied. Also it can be used if the observations or their relations are vague. In this study, a model called Fuzzy Logistic Regression Based on Revised Tanaka's Fuzzy Linear Regression Model is proposed. In this regard, the methodology and formulation of the proposed model is explained in detail and the revised Tanaka's regression model is used to estimate the parameters. The Revised Tanaka's Regression model is an extension of Tanaka's Regression Model in which the objection function is developed. An application is performed on birth weight data set. Also, an application of diabetes data set used in Pourahmad et al.'s study was conducted via our proposed data set. The validity of the model is shown by the help of goodness of fit criteria called Mean Degree Memberships (MDM).en_US
dc.description.sponsorshipAnadolu University Scientific Research Projects [1307F285]en_US
dc.description.sponsorshipThis work was supported by Anadolu University Scientific Research Projects (Project Number: 1307F285)en_US
dc.language.isoengen_US
dc.publisherUNIV SISTAN & BALUCHESTANen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy logistic regressionen_US
dc.subjectRevised Tanaka regression modelen_US
dc.subjectMDM criteriaen_US
dc.titleA NEW APPROACH FOR PARAMETER ESTIMATION IN FUZZY LOGISTIC REGRESSIONen_US
dc.typearticleen_US
dc.relation.journalIRANIAN JOURNAL OF FUZZY SYSTEMSen_US
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.startpage91en_US
dc.identifier.endpage102en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.department-temp[Atalik, G. -- Senturk, S.] Anadolu Univ, Dept Stat, Eskisehir, Turkey -- [Atalik, G.] Amasya Univ, Dept Stat, Amasya, Turkeyen_US


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