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
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Christophe Croux
K. U. Leuven, Faculty of Business and
Economics
Naamsestraat 69,
B-3000
Leuven, Belgium
christophe.croux@econ.kuleuven.be
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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.
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
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