The shooting S-estimator for robust regression

TL;DR

The paper introduces the shooting S-estimator, a robust regression method designed to handle situations with many contaminated observations in a few predictor variables, improving robustness over traditional S-estimators.

Contribution

It proposes a novel shooting S-estimator that combines coordinate descent with S-regression to address componentwise contamination in predictors.

Findings

Effective against componentwise contamination

Handles high contamination in predictor variables

Trade-off: loses regression equivariance

Abstract

To perform multiple regression, the least squares estimator is commonly used. However, this estimator is not robust to outliers. Therefore, robust methods such as S-estimation have been proposed. These estimators flag any observation with a large residual as an outlier and downweight it in the further procedure. However, a large residual may be caused by an outlier in only one single predictor variable, and downweighting the complete observation results in a loss of information. Therefore, we propose the shooting S-estimator, a regression estimator that is especially designed for situations where a large number of observations suffer from contamination in a small number of predictor variables. The shooting S-estimator combines the ideas of the coordinate descent algorithm with simple S-regression, which makes it robust against componentwise contamination, at the cost of failing the regression equivariance property.