Robustness for Dummies
AbstractIn the robust statistics literature, a wide variety of models have been devel- oped to cope with outliers in a rather large number of scenarios. Nevertheless, a recurrent problem for the empirical implementation of these estimators is that optimization algorithms generally do not perform well when dummy vari- ables are present. What we propose in this paper is a simple solution to this involving the replacement of the sub-sampling step of the maximization procedures by a projection-based method. This allows us to propose robust estimators involving categorical variables, be they explanatory or dependent. Some Monte Carlo simulations are presented to illustrate the good behavior of the method.
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Bibliographic InfoPaper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number ECARES 2012-015.
Length: 27 p.
Date of creation: May 2012
Date of revision:
Publication status: Published by:
S-estimators; Robust Regression; Dummy Variables; Outliers;
Other versions of this item:
- Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte, 2012. "Robustness for Dummies," Working Papers 1206, University of Namur, Department of Economics.
- Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte, 2012. "Robustness for dummies," United Kingdom Stata Users' Group Meetings 2012 09, Stata Users Group.
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- Croux, Christophe & Haesbroeck, Gentiane, 2003. "Implementing the Bianco and Yohai estimator for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 273-295, October.
- Rousseeuw, Peter J. & Wagner, Joachim, 1994. "Robust regression with a distributed intercept using least median of squares," Computational Statistics & Data Analysis, Elsevier, vol. 17(1), pages 65-76, January.
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