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

• Autores: Vincenzo Verardi, Catherine Vermandele
• Localización: The Stata journal, ISSN 1536-867X, Vol. 18, Nº. 3, 2018, págs. 517-532
• Idioma: inglés
• DOI: 10.1177/1536867x1801800303
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• Dialnet Métricas: 1 Cita
• Resumen

  • 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.