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Robustness for Dummies

  • Vincenzo Verardi

    ()

    (Center for Research in the Economics of Development, University of Namur; European Center for Advanced Research in Economics and Statistics, Universite Libre de Bruxelles)

  • Marjorie Gassner

    ()

    (European Center for Advanced Research in Economics and Statistics, Universite Libre de Bruxelles)

  • Darwin Ugarte

    ()

    (Center for Research in the Economics of Development, University of Namur)

In the robust statistics literature, a wide variety of models have 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 poper is a simple solution to this involving the replacement of the sub-sampling step of the maximization procedure by a projection-based method. This allows us to propose robust estimators involving categorical variables, be they explanatory of dependent. Some Monte Carlo simulations are presented to illustrate the good behavior of the method.

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File URL: http://www.fundp.ac.be/eco/economie/recherche/wpseries/wp/1206
File Function: First version, 2012
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Paper provided by University of Namur, Department of Economics in its series Working Papers with number 1206.

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Length: 27 pages
Date of creation: May 2012
Date of revision:
Handle: RePEc:nam:wpaper:1206
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  1. 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.
  2. 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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