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Benchmark testing of algorithms for very robust regression: FS, LMS and LTS

Torti, Francesca, Perrotta, Domenico, Atkinson, Anthony C. and Riani, Marco (2012) Benchmark testing of algorithms for very robust regression: FS, LMS and LTS. Computational Statistics and Data Analysis, 56 (8). pp. 2501-2512. ISSN 0167-9473

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Abstract

The methods of very robust regression resist up to 50% of outliers. The algorithms for very robust regression rely on selecting numerous subsamples of the data. New algorithms for LMS and LTS estimators that have increased computational efficiency due to improved combinatorial sampling are proposed. These and other publicly available algorithms are compared for outlier detection. Timings and estimator quality are also considered. An algorithm using the forward search (FS) has the best properties for both size and power of the outlier tests.

Item Type: Article
Official URL: http://www.journals.elsevier.com/computational-sta...
Additional Information: © 2012 Elsevier
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 04 Apr 2012 10:50
URL: http://eprints.lse.ac.uk/42970/

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