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Robustness for Dummies

Author

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  • Vincenzo Verardi
  • Marjorie Gassner
  • Darwin Ugarte Ontiveros

Abstract

In 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.

Suggested Citation

  • Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte Ontiveros, 2012. "Robustness for Dummies," Working Papers ECARES ECARES 2012-015, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:eca:wpaper:2013/117087
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    References listed on IDEAS

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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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    Cited by:

    1. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    2. Darwin Ugarte Ontiveros & Ruth Marcela Aparicio de Guzmán, 2020. "Técnicas Robustas y No Robustas para Identificar Outliers en el Análisis de Regresión," Investigación & Desarrollo 0320, Universidad Privada Boliviana, revised Nov 2020.

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    More about this item

    Keywords

    S-estimators; Robust Regression; Dummy Variables; Outliers;
    All these keywords.

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