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.

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.

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.

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},
     zbl = {1358.62053},
     mrnumber = {3554075},
     language = {en},
     url = {http://www.numdam.org/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://www.numdam.org/item/JSFS_2016__157_2_90_0/

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