Robustness for dummies
AbstractIn the robust statistics literature, a wide variety of models has been developed 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 variables are present. What we propose in this paper is a simple solution to this involving the replacement of the subsampling 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 Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2012 with number 09.
Date of creation: 22 Sep 2012
Date of revision:
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 Ontiveros, 2012. "Robustness for Dummies," Working Papers ECARES ECARES 2012-015, ULB -- Universite Libre de Bruxelles.
- NEP-ALL-2012-10-20 (All new papers)
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- 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.
- 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.
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