تقييم أداء بعض الطرائق الحصينة لتقدير معاملات نموذج الانحدار الخطي بوجود القيم الشاذة

حسن  الشمري; ريام  مسير

doi:10.36322/jksc.182.25770

Authors

Prof Dr. Hassan Sami Oraibi Al-Shammari Faculty of Administration and Economics/University of Al-Qadisiyah
Researcher Riyam Khaled Hussein Musair Faculty of Administration and Economics/University of Al-Qadisiyah

DOI:

https://doi.org/10.36322/jksc.182.25770

Keywords:

Multiple linear regression, Ordinary least squares, Outliers, Robust methods

Abstract

Multiple linear regression (MLR) is a fundamental statistical model used to study the effect of several independent variables on a dependent variable by estimating parameters using the least squares method. Several robust estimation methods, such as M-Huber, LMS, and LTS, have been developed to handle outliers, but these methods still suffer from limitations when dealing with high leverage points. To overcome these challenges, the researcher proposed a new method based on calculating robust Mahalanobis distances using MVE and RMVN and employing weights derived from the weighting functions proposed by Giloni and Arslan. These weights are used to weight the data before estimating parameters using robust methods, thus enhancing the accuracy of the estimates and reducing the impact of leverage points and outliers. To test the effectiveness of this algorithm, the researcher conducted a simulation study on market capitalization data and compared the calculated estimates with the traditional MM method, focusing on calculating the mean squared error (MSE) to determine the optimal method.

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