Title: The shooting S-estimator for robust regression
Authors: Oellerer, Viktoria ×
Alfons, Andreas
Croux, Christophe #
Issue Date: 2016
Publisher: Physica-Verlag
Series Title: Computational Statistics vol:31 issue:3 pages:829-844
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 ag 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.
ISSN: 0943-4062
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
shootingS_final.pdfThe shooting S-estimator for robust regression Published 435KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science