Assessing the Effectiveness of Robust Regression Techniques in Handling Outlier-Contaminated Datasets

Posted: Mar 08, 2012

• Elizabeth Smith
• Ella Anderson
• Ella Walker

Abstract

Robust regression methodologies represent a critical advancement in statistical analysis, specifically designed to address the limitations of ordinary least squares regression when underlying assumptions are violated. The presence of outliers in datasets remains a pervasive challenge across numerous disciplines, from environmental monitoring to financial modeling and biomedical research. Traditional regression techniques, while computationally efficient and theoretically well-established, demonstrate significant vulnerability to outlier influence, often resulting in biased parameter estimates, inflated standard errors, and compromised predictive performance. This research undertakes a comprehensive evaluation of robust regression techniques, examining their relative effectiveness across varying contamination scenarios and dataset characteristics. The fundamental problem addressed in this study concerns the selection of appropriate robust regression methods given specific dataset properties and contamination patterns. While numerous robust techniques have been developed since the pioneering work of Huber (1964) and Hampel (1974), a systematic comparative framework for method selection remains elusive. Practitioners often face uncertainty when choosing between alternative robust approaches, particularly when dealing with complex contamination patterns that include both vertical outliers and leverage points. This research gap is particularly consequential given the increasing complexity of real-world datasets and the critical importance of reliable statistical inference in decision-making contexts.