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The Stata Journal
Volume 18 Number 3: pp. 517-532

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Univariate and multivariate outlier identification for skewed or heavy-tailed distributions

Vincenzo Verardi
Université de Namur
Namur, Belgium
Catherine Vermandele
Université libre de Bruxelles
Brussels, Belgium
Abstract.  In univariate and in multivariate analyses, it is difficult to identify outliers in the case of skewed or heavy-tailed distributions. In this article, we propose simple univariate and multivariate outlier identification procedures that perform well with these types of distributions while keeping the computational complexity low. We describe the commands gboxplot (univariate case) and sdasym (multivariate case), which implement these procedures in Stata.
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View all articles by these authors: Vincenzo Verardi, Catherine Vermandele

View all articles with these keywords: gboxplot, sdasym, box plot, generalized box plot, outlier detection, outlyingness, projection, Tukey g-and-h distribution

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