Robust Regression: Enhancing Model Resilience Against Outliers and Influential Data Points

Introduction

In statistical modeling, regression analysis is a fundamental technique used to understand relationships between variables and to make predictions. However, traditional regression methods, such as ordinary least squares (OLS), can be significantly affected by outliers and influential data points. This paper will explore the concept of robust regression, a statistical approach designed to mitigate the adverse effects of such anomalies in data. The purpose of this report is to highlight the importance of robust regression techniques, examine various robust methods, and discuss their effectiveness in enhancing model resilience. By addressing these issues, it will become evident that robust regression is a vital tool for analysts and researchers striving for accuracy and reliability in their predictive models.

Main Body

Outliers are data points that deviate markedly from the rest of the dataset. They can arise from measurement errors, variability in the data, or represent true but rare occurrences. Influential data points, on the other h