MM_REGRESS: Stata module to compute robust regression estimates
Vincenzo Verardi, Christophe Croux
Abstract
Vincenzo Verardi, Christophe Croux
Abstract
In regression analysis, the presence of outliers in the data set 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 commands rreg and qreg. Unfortunately, these methods only resist to some specific types of outliers and turn out to be ineffective under alternative scenarios. In this package we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that allows recognizing the type of detected outliers.
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In regression analysis, the presence of outliers in the data set 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 commands rreg and qreg. Unfortunately, these methods only resist to some specific types of outliers and turn out to be ineffective under alternative scenarios. In this package we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that allows recognizing the type of detected outliers.
Key concepts: Outlier, Estimator, Robust regression, Robust statistics, Computer science, Regression, Set (abstract data type), Least trimmed squares