(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The ordinary least squares method (OLS) is one of the most common methods for estimating the coefficients of linear regression models. However, it is sensitive and not robust against the existence of outliers. Therefore, several robust estimation methods have been used and then represented by M-estimation using different objective functions. In this paper, a number of alternative robust methods have been suggested that represented by using Gastwirth’s location estimator instead of the mean in OLS and instead of the median in different Mestimation methods. In addition to repeating the Hubers’ M-estimation method (first method) until converged results are reached. A Monte-Carlo simulation study was employed to evaluate the performance of different estimation methods depending on the MSE of regression coefficients.