Title: Benchmark testing of algorithms for very robust regression: FS, LMS and LTS
Authors: TORTI FrancescaPERROTTA DomenicoATKINSON Anthony C.RIANI Marco
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS vol. 56 no. 8 p. 2501-2512
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2012
JRC Publication N°: JRC67506
ISSN: 0167-9473
URI: http://www.sciencedirect.com/science/article/pii/S0167947312000680
http://publications.jrc.ec.europa.eu/repository/handle/JRC67506
DOI: 10.1016/j.csda.2012.02.003
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

Files in This Item:
There are no files associated with this item.


Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.