A characterization of the extension principle
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Information Sciences: an International Journal
Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
Multiobjective fuzzy linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems
Properties of certain fuzzy linear regression methods
Fuzzy Sets and Systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Evaluation of fuzzy linear regression models by comparing membership functions
Fuzzy Sets and Systems
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Hybrid fuzzy least-squares regression analysis and its relibabilty measures
Fuzzy Sets and Systems
Fuzzy least-squares linear regression analysis using shape preserving operations
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Modeling for Control
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
A fuzzy linear regression model with better explanatory power
Fuzzy Sets and Systems - Information processing
A new approach to fuzzy regression models with application to business cycle analysis
Fuzzy Sets and Systems
Extended fuzzy regression models using regularization method
Information Sciences—Informatics and Computer Science: An International Journal
Inferring operating rules for reservoir operations using fuzzy regression and ANFIS
Fuzzy Sets and Systems
Fuzzy nonparametric regression based on local linear smoothing technique
Information Sciences: an International Journal
Multiple regression with fuzzy data
Fuzzy Sets and Systems
Fuzzy regression model of R&D project evaluation
Applied Soft Computing
Least squares estimation of a linear regression model with LR fuzzy response
Computational Statistics & Data Analysis
Modeling of thermal comfort in air conditioned rooms by fuzzy regression analysis
Mathematical and Computer Modelling: An International Journal
An enhanced fuzzy linear regression model with more flexible spreads
Fuzzy Sets and Systems
Information Sciences: an International Journal
A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals
Information Sciences: an International Journal
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
Robust fuzzy regression analysis
Information Sciences: an International Journal
Tackling outliers in granular box regression
Information Sciences: an International Journal
A two-stage approach for formulating fuzzy regression models
Knowledge-Based Systems
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Fuzzy regression models have been applied to operational research (OR) applications such as forecasting. Some of previous studies on fuzzy regression analysis obtain crisp regression coefficients for eliminating the problem of increasing spreads for the estimated fuzzy responses as the magnitude of the independent variable increases; however, they still cannot cope with the situation of decreasing or variable spreads. This paper proposes a three-phase method to construct the fuzzy regression model with variable spreads to resolve this problem. In the first phase, on the basis of the extension principle, the membership functions of the least-squares estimates of regression coefficients are constructed to conserve completely the fuzziness of observations. In the second phase, then they are defuzzified by the center of gravity method to obtain crisp regression coefficients. In the third phase, the error terms of the proposed model are determined by setting each estimated spread equals its corresponding observed spread. Furthermore, the Mamdani fuzzy inference system is adopted for improving the accuracy of its forecasts. Compared to the previous studies, the results from five examples and an application example of Japanese house prices show that the proposed fuzzy linear regression model has higher explanatory power and forecasting performance.