Document Type : Research Paper
Authors
Behbahan Khatam Alanbia university of technology
Abstract
Both traditional and fuzzy regression analyses have demonstrated the significant characteristics of the least-squares methodology as a method for parameter estimation.} The presence of outliers in the sample and/or minor variations in the dataset might impact the behaviour and characteristics of the least-squares estimators (LSE). In contrast, robust approaches provide estimators of the parameters that are resilient to the aforementioned unfavourable effects. This study aims to expand upon the Theil-Sen estimator in fuzzy regression analysis, with the objective of obtaining consistent findings even when outliers are present. \rd{ We demonstrate the effectiveness of the suggested technique through simulation experiments and real-world examples, comparing it to commonly used fuzzy regression models. The applicative examples are based on hydrology and atmospheric environment datasets. We also show the sensitivity analysis of the estimated parameters using a Monte-Carlo simulation study, demonstrating the effectiveness of the suggested estimators in comparison to other established approaches in the field of fuzzy regression analysis. The results showed that the Theil-Sen estimator (TSE) is very effective in cases where there are outliers, and the calculation error is smaller compared to other methods.
Keywords
Main Subjects
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