Seung Hoe Choi

Fuzzy logistic regression with least absolute deviations estimators

This study is an investigation of fuzzy logistic regression model for crisp input and fuzzy output data. The response variable is non-precise and is measured by linguistic terms. Especially this research develops least absolute deviations (LAD) method for modeling and compares the results with the least squares estimation (LSE) method. For these, two estimation methods, min–max method and fitting method, are provided in this research. This study presents new goodness-of-fit indices which are called measure of performance based on fuzzy distance

Fuzzy linear regression using rank transform method

(M_p)

Fuzzy Least Squares Estimation with New Fuzzy Operations

(Mp) and index of sensitivity

Fuzzy Regression Model Using Trapezoidal Fuzzy Numbers for Re-auction Data

(I_S)

Optimal properties of a fuzzy least estimator based on new operations

(IS). The study gives two numerical examples in real clinical studies about systematic lupus erythematosus and the other one in the field of nutrition to explain the proposed methods. In addition, we investigate the sensitivity of two estimation methods in the case of outliers by a numerical example.

Interval regression using symmetric difference

This article introduces a general fuzzy regression model, which separates the response function on a mode and spreads of an α-level set for an observed fuzzy number, to estimate a fuzzy relation between two fuzzy random variables. We construct the general fuzzy regression model using least squares estimation and best response functions on the mode and spread of an α-level set for the fuzzy number when the response variable is an LR-fuzzy number and independent variables are crisp numbers. Then we derive a crisp mean and variance of the predicted fuzzy number, and compare the accuracy of our proposed fuzzy regression model with other fuzzy regression models suggested by many authors.