Enhancing Parameter Estimation for Fuzzy Robust Regression in the Presence of Outliers

This study presents an enhanced algorithm for parameter estimation in fuzzy robust regression (FRR), aimed at improving the reliability of estimates in the presence of outliers. The standard approach of using ordinary least squares (OLS) struggles when dealing with both outlier effects and the uncertainty inherent in data. By combining traditional FRR analysis with the Huber loss function, this research addresses these challenges effectively. The performance of the algorithm is evaluated using real-world datasets and a simulation study, demonstrating its ability to minimize the impact of outliers. Furthermore, the algorithm not only outperforms OLS but also serves as a robust alternative to traditional methods, including Huber, Hampel, Tukey, Andrews, MM-estimates and existing FRR approaches.