The Impact of Robust Statistical Methods on Model Performance in the Presence of Data Outliers and Noise

Posted: Jun 09, 2022

• Levi Lee
• Olivia Adams
• Sarah Miller

Abstract

The proliferation of machine learning applications across diverse domains has exposed a critical vulnerability in conventional modeling approaches: their sensitivity to data contamination in the form of outliers and noise. Real-world datasets frequently contain anomalous observations that deviate significantly from the underlying data distribution, whether due to measurement errors, data entry mistakes, equipment malfunctions, or genuine rare events. Traditional statistical methods and machine learning algorithms, optimized for idealized conditions, often exhibit substantial performance degradation when confronted with such contaminated data. This research addresses this fundamental challenge by developing and evaluating a comprehensive framework of robust statistical methods specifically designed to enhance model resilience without sacrificing predictive accuracy on clean data.