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

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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
Christophe Croux
K. U. Leuven, Faculty of Business and Economics
Naamsestraat 69, B-3000
Leuven, Belgium
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.
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View all articles with these keywords: mmregress, sregress, msregress, mregress, mcd, S-estimators, MM-estimators, outliers, robustness

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