Finding Regression Outliers With FastRCS

TL;DR

FastRCS is a new, efficient method for detecting outliers in linear regression that is robust against outliers and outperforms existing methods in simulations and real data applications.

Contribution

The paper introduces FastRCS, a fast and robust algorithm for regression outlier detection based on the Residual Congruent Subset criterion, which is insensitive to outliers.

Findings

FastRCS outperforms competitors in simulation studies.

FastRCS effectively detects outliers in real data.

The method is computationally efficient and affine equivariant.

Abstract

The Residual Congruent Subset (RCS) is a new method for finding outliers in the linear regression setting. Like many other outlier detection procedures, RCS searches for a subset which minimizes a criterion. The difference is that the new criterion was designed to be insensitive to the outliers. RCS is supported by FastRCS, a fast regression and affine equivariant algorithm which we also detail. Both an extensive simulation study and two real data applications show that FastRCS performs better than its competitors.