Title: A Parametric Framework for the Comparison of Methods of Very Robust Regression
Authors: ATKINSON Anthony C.RIANI MarcoPERROTTA Domenico
Citation: STATISTICAL SCIENCE vol. 29 no. 1 p. 128–143
Publisher: INST MATHEMATICAL STATISTICS
Publication Year: 2014
JRC N°: JRC77595
ISSN: 0883-4237
URI: http://projecteuclid.org/euclid.ss/1399645741
http://publications.jrc.ec.europa.eu/repository/handle/JRC77595
DOI: 10.1214/13-STS437
Type: Articles in periodicals and books
Abstract: There are several methods for obtaining very robust estimates of regression parameters that asymptotically resist 50% of outliers in the data. Differences in the behaviour of these algorithms depend on the distance between the regression data and the outliers. We introduce a parameter that defines a parametric path in the space of models and enables us to study, in a systematic way, the properties of estimators as the groups of data move from being far apart to close together. We examine, as a function of , the variance and squared bias of five estimators and we also consider their power when used in the detection of outliers. This systematic approach provides tools for gaining knowledge and better understanding of the properties of robust estimators.
JRC Directorate:Space, Security and Migration

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