Geodesic
Outlier identification for skewed and/or heavy-tailed unimodal multivariate distributions
[Identification de valeurs extrêmes pour des distributions multivariées unimodales asymétriques et/ou à queues lourdes]
Journal de la société française de statistique, Tome 157 (2016) no. 2, pp. 90-114.

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In multivariate analysis, it is very difficult to identify outliers in case of skewed and/or heavy-tailed distributions. In this paper, we propose a very simple outlier identification tool that works with these types of distributions and that keeps the computational complexity low.

L’identification de valeurs extrêmes s’avère particulièrement délicate en analyse multivariée lorsque la distribution sous-jacente est asymétrique et/ou à queues lourdes. Cet article présente une méthode d’identification extrêmement simple, bien adaptée à ce type de distribution et qui n’exige qu’une faible complexité calculatoire.

Keywords: outlier identification, skewed multivariate distribution, heavy-tailed multivariate distribution, Tukey $g$-and-$h$ distribution
Mots-clés : identification de valeurs extrêmes, distribution multivariée asymétrique, distribution multivariée à queues lourdes, distribution de Tukey $g$-et-$h$ 
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     author = {Verardi, Vincenzo and Vermandele, Catherine},
     title = {Outlier identification for skewed and/or heavy-tailed unimodal multivariate distributions},
     journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
     pages = {90--114},
     publisher = {Soci\'et\'e fran\c{c}aise de statistique},
     volume = {157},
     number = {2},
     year = {2016},
     mrnumber = {3554075},
     zbl = {1358.62053},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JSFS_2016__157_2_90_0/}
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Verardi, Vincenzo; Vermandele, Catherine. Outlier identification for skewed and/or heavy-tailed unimodal multivariate distributions. Journal de la société française de statistique, Tome 157 (2016) no. 2, pp. 90-114. http://geodesic.mathdoc.fr/item/JSFS_2016__157_2_90_0/

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