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
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Multiobjective fuzzy linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems
A proposal for a defuzzification strategy by the concept of sensitivity analysis
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
Properties of certain fuzzy linear regression methods
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Evaluation of fuzzy linear regression models by comparing membership functions
Fuzzy Sets and Systems
A linear regression model using triangular fuzzy number coefficients
Fuzzy Sets and Systems
Fuzzy least-squares linear regression analysis using shape preserving operations
Information Sciences—Informatics and Computer Science: An International Journal
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
Multiple regression with fuzzy data
Fuzzy Sets and Systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy regression models using the least-squares method based on the concept of distance
IEEE Transactions on Fuzzy Systems
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Fuzzy regression models have been widely applied to explain the relationship between explanatory variables and responses in fuzzy environments. This paper proposes a simple two-stage approach for constructing a fuzzy regression model based on the distance concept. Crisp numbers representing the fuzzy observations are obtained using the defuzzification method, and then the crisp regression coefficients in the fuzzy regression model are determined using the conventional least-squares method. Along with the crisp regression coefficients, the proposed fuzzy regression model contains a fuzzy adjustment variable so that the model can deal with the fuzziness from fuzzy observations in order to reduce the fuzzy estimation error. A mathematical programming model is formulated to determine the fuzzy adjustment term in the proposed fuzzy regression model to minimize the total estimation error based on the distance concept. Unlike existing approaches that only focus on positive coefficients, the problem of negative coefficients in the fuzzy regression model is taken into account and resolved in the solution procedure. Comparisons with previous studies show that the proposed fuzzy regression model has the highest explanatory power based on the total estimation error using various criteria. A real-life dataset is adopted to demonstrate the applicability of the proposed two-stage approach in handling a problem with negative coefficients in the fuzzy regression model and a large number of fuzzy observations.