Two Stage Robust Dawoud – Kibria Estimator for Handling multicollinearity and outliers in the linear Regression model. | ||||
| مجلة البحوث التجارية | ||||
| Article 27, Volume 47, Issue 1, January 2025, Page 72-100 PDF (1.07 MB) | ||||
| Document Type: تجاریة کل ما یتعلق بالعلوم التجاریة | ||||
| DOI: 10.21608/zcom.2024.317679.1378 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Enas Goda Mohamed 
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| كلية التجارة شعبة الإحصاء والتأمين جامعة الزقازيق | ||||
| Abstract | ||||
| Abstract In the linear regression model, the least-squares (LS) estimator is commonly used to estimate regression parameters. However, LS becomes unreliable and unfavorable when the model is affected by multicollinearity and outliers simultaneously. Numerous authors have proposed various estimators to address the challenges of multicollinearity and outliers in linear regression models. This paper introduces an alternative robust regression estimator, called the Two-Stage Robust Dawoud–Kibria estimator, designed to address the two issues simultaneously. We performed theoretical comparisons, conducted simulations under different scenarios to illustrate the effectiveness of the proposed estimator. Theoretical analysis and simulation results indicate that the proposed estimator outperforms other regression estimators under certain conditions when both multicollinearity and outlier issues are present, based on the mean squared error criterion. Key words: Two-Stage Robust Dawoud–Kibria estimator (MMDK), Robust Dawoud–Kibria estimator (MDK), Robust Liu (M-Liu), Robust Ridge (M-Ridge), Robust Özkale–Kaçiranlar (MOK), M-estimator (ME), .Non-Robust estimators, multicollinearity, outliers, Mean squared errors(MSEs) | ||||
| Keywords | ||||
| .Multicollinearity; Outliers; MSE; MMDK estimator; Robust estimators | ||||
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