Robustness for Dummies
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.
|Date of creation:||May 2012|
|Date of revision:|
|Publication status:||Published by:|
|Contact details of provider:|| Postal: Av. F.D., Roosevelt, 39, 1050 Bruxelles|
Phone: (32 2) 650 30 75
Fax: (32 2) 650 44 75
Web page: http://difusion.ulb.ac.be
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eca:wpaper:2013/117087. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Benoit Pauwels)
If references are entirely missing, you can add them using this form.