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Title: Sparse least trimmed squares regression for analyzing high-dimensional large data sets
Authors: Alfons, Andreas ×
Croux, Christophe
Gelper, Sarah #
Issue Date: 2013
Publisher: Institute of Mathematical Statistics
Series Title: Annals of Applied Statistics vol:7 issue:1 pages:226-248
Abstract: Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data. This paper combines robust regression and sparse model estimation. A robust and sparse estimator is introduced by adding an L1 penalty on the coefficient estimates to the well-known least trimmed squares (LTS) estimator. The breakdown point of this sparse LTS estimator is derived, and a fast algorithm for its computation is proposed. In addition, the sparse LTS is applied to protein and gene expression data of the NCI-60 cancer cell panel. Both a simulation study and the real data application show that the sparse LTS has better prediction performance than its competitors in the presence of leverage points.
ISSN: 1932-6157
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

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