Home
Home  >>  Archives  >>  Volume 9 Number 3  >>  st0173

The Stata Journal
Volume 9 Number 3: pp. 439-453



Subscribe to the Stata Journal
cover

Robust regression in Stata

Vincenzo Verardi
University of Namur (CRED)
and Université Libre de
Bruxelles (ECARES and CKE)
Rempart de la Vierge 8,
B-5000
Namur, Belgium
vverardi@fundp.ac.be
Christophe Croux
K. U. Leuven, Faculty of Business and
Economics
Naamsestraat 69,
B-3000
Leuven, Belgium
christophe.croux@econ.kuleuven.be
Abstract.  In regression analysis, the presence of outliers in the dataset can strongly distort the classical least-squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the rreg and qreg commands. Unfortunately, these methods resist only some specific types of outliers and turn out to be ineffective under alternative scenarios. In this article, we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that recognizes the type of detected outliers.
Terms of use     View this article (PDF)

View all articles by these authors: Vincenzo Verardi, Christophe Croux

View all articles with these keywords: mmregress, sregress, msregress, mregress, mcd, S-estimators, MM-estimators, outliers, robustness

Download citation: BibTeX  RIS

Download citation and abstract: BibTeX  RIS

Contact StataCorp

Contact service@stata-journal.com if you have questions about the Stata Journal.

© Copyright 2001–2014 StataCorp LP.   Terms of use.   Privacy policy.