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

Authors:
Francesca Torti;Domenico Perrotta;Anthony C. Atkinson;Marco Riani
Affiliations:
Dipartimento di Economia, Universití di Parma, Italy;European Commission, Joint Research Centre, Ispra, Italy;The London School of Economics, WC2A 2AE London, United Kingdom;Dipartimento di Economia, Universití di Parma, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2012

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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.