The shooting S-estimator for robust regression
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.
Cite
@article{arxiv.1506.01223,
title = {The shooting S-estimator for robust regression},
author = {Viktoria Öllerer and Andreas Alfons and Christophe Croux},
journal= {arXiv preprint arXiv:1506.01223},
year = {2025}
}