Comparison of Robust Methods for Multiple Linear Regression Models

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This study compares ordinary least squares and robust estimation methods, revealing the Huber method's effectiveness against data contamination.

The Huber method significantly outperformed other estimation techniques at contamination levels of 7% or more.
The presence of outliers can significantly reduce the accuracy of ordinary least squares estimates.
Robust estimation methods can provide more reliable estimates in the presence of contaminated data.
This analysis highlights the importance of choosing appropriate methods for statistical modeling to ensure accuracy.

Sadeq et al. (2025) studied this question.

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This study compares ordinary least squares and robust estimation methods, revealing the Huber method's effectiveness against data contamination.