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arXiv · 1506.01223

The shooting S-estimator for robust regression

Viktoria Öllerer·Andreas Alfons·Christophe Croux
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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.

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Viktoria Öllerer, Andreas Alfons, Christophe Croux. 2015-06-03. The shooting S-estimator for robust regression. https://doi.org/10.1007/s00180-015-0593-7

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