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 $\alpha$-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.

本文提出了一种具有模糊截距和非模糊回归系数的鲁棒多元回归模型。具有模糊随机变量和$\alpha$的值 $\mathrm{大号}\mathrm{[R}$还引入了-fuzzy以估计模型的组成部分。一些常用的拟合优度标准也用于评估所提出方法的性能。通过包括仿真研究在内的三个数值示例，将该方法的有效性与一些常见的模糊鲁棒回归模型进行了比较。数值结果表明，与其他现有的鲁棒回归方法相比，该模型对异常值的敏感性较低，并且其精度较高。