A new approach for parameter estimation in fuzzy logistic regression and an application

Models define the relationship between dependent and independent variables called regression models. However, if the considered dependent variable is categorical, it's named logistic regression. To be able to apply logistic regression, certain hypothesis' should be provided. Composed of the fuzzy set and statistical theories, fuzzy logistic regression is a method can be used when these hypothesis' can't be provided or data include structural vagueness. In this thesis fuzzy logistic regression model's theoretical structure mentioned and a new approach is suggested for this model's unknown parameter's estimation. Suggested approach is applied to birth weight and it's validity is interpreted by the help of a goodness of fit test called Mean Degree Membership.