Robustness for Dummies
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.
|Date of creation:||May 2012|
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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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