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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.

Suggested Citation

  • Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte, 2012. "Robustness for Dummies," Working Papers 1206, University of Namur, Department of Economics.
  • Handle: RePEc:nam:wpaper:1206

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    References listed on IDEAS

    1. 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.
    2. 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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    Cited by:

    1. Darwin Ugarte Ontiveros & Gustavo Canavire-Bacarreza & Luis Castro PeƱarrieta, 2017. "Outliers in semi-parametric Estimation of Treatment Effects," DOCUMENTOS DE TRABAJO CIEF 015810, UNIVERSIDAD EAFIT.

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    S-estimators; robust regression; dummy variable; outliers;

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