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.

Cellwise outliers, Componentwise contamination, Coordinate descent algorithm, Regression S-estimation, Shooting algorithm
dx.doi.org/10.1007/s00180-015-0593-7, hdl.handle.net/1765/85974
ERIM Top-Core Articles
Computational Statistics
Erasmus School of Economics

Öllerer, V, Alfons, A, & Croux, C. (2016). The shooting S-estimator for robust regression. Computational Statistics, 31(3), 829–844. doi:10.1007/s00180-015-0593-7