Parameter Estimation of the Fuzzy Logistic Regressive Model with LR Typed Fuzzy Coefficients
Abstract: The classical logistic regression model is appropriate for the problems of binary variable. Since the observations were not crisp value, response variable was often between 0 and 1 and no probability distribution can be considered for response variable; hence error can not be completely regarded as random aspect. Therefore, by combining the classical logistic regression model with fuzzy sets theory, a fuzzy logistic regression model of crisp input and fuzzy output data is constructed; the coefficients and outputs are LR type fuzzy numbers. Considering the possibilities of success instead of the probabilities, the possibilities of success are described by some linguistic terms. Then the distance between two fuzzy numbers is constructed by cut sets. The least squares estimation of fuzzy parameters is obtained in proposed model based on the distance. Finally, the capability index MSI = 0.54 showed that the proposed model is effective of an ordinary one in the modeling Lupus.
文章引用: 陈 怡 , 魏立力 (2016) 系数为LR-型模糊数的模糊Logistic回归模型的参数估计。 运筹与模糊学， 6， 27-35. doi: 10.12677/ORF.2016.61004
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