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|Title:||Benchmark testing of algorithms for very robust regression: FS, LMS and LTS|
|Authors:||TORTI Francesca; PERROTTA Domenico; ATKINSON Anthony C.; RIANI Marco|
|Citation:||COMPUTATIONAL STATISTICS & DATA ANALYSIS vol. 56 no. 8 p. 2501-2512|
|Publisher:||ELSEVIER SCIENCE BV|
|JRC Publication N°:||JRC67506|
|Type:||Articles in Journals|
|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. We describe new algorithms for LMS and LTS estimators that have increased efficiency due to improved combinatorial sampling. These and other publicly available algorithms are compared for outlier detection. An algorithm using the forward search has the best properties for both size and power of the outlier tests.|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
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