Economics at your fingertips  

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

Vincenzo Verardi and Catherine Vermandele ()
Additional contact information
Catherine Vermandele: Université libre de Bruxelles

Stata Journal, 2018, vol. 18, issue 3, 517-532

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.

Keywords: gboxplot; sdasym; box plot; generalized box plot; outlier detection; outlyingness; projection; Tukey g-and-h distribution (search for similar items in EconPapers)
Date: 2018
Note: to access software from within Stata, net describe
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) link to article purchase

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Stata Journal is currently edited by H. Joseph Newton and Nicholas J. Cox

More articles in Stata Journal from StataCorp LP
Bibliographic data for series maintained by Christopher F. Baum ().

Page updated 2020-03-29
Handle: RePEc:tsj:stataj:y:18:y:2018:i:3:p:517-532