Abstract
In the present paper, a novel robust multiple regression model with fuzzy intercepts and non-fuzzy regression coefficients was proposed. A two-stage robust procedure adopted with fuzzy random variables and α-values of LR-fuzzy was also introduced to estimate the components of the model. Some common goodness-of-fit criteria were also used to evaluate the performance of the proposed method. The effectiveness of the proposed method was compared to some common fuzzy robust regression models through three numerical examples including a simulation study. The numerical results indicated the lower sensitivity of the proposed model to outliers and its higher precision compared to the other existing robust regression methods.
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