High-dimensional test for normality

Jérémie Kellner 1, 2 Alain Celisse 2
1 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : A new goodness-of-fit test for normality in high-dimension (and Reproducing Kernel Hilbert Space) is proposed. It shares common ideas with the Maximum Mean Discrepancy (MMD) it outperforms both in terms of computation time and applicability to a wider range of data. Theoretical results are derived for the Type-I and Type-II errors. They guarantee the control of Type-I error at prescribed level and an exponentially fast decrease of the Type-II error. Synthetic and real data also illustrate the practical improvement allowed by our test compared with other leading approaches in high-dimensional settings.
Type de document :
Communication dans un congrès
Journées des Statistiques, Jun 2014, Rennes, France
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Contributeur : Jérémie Kellner <>
Soumis le : vendredi 5 décembre 2014 - 15:19:46
Dernière modification le : mardi 3 juillet 2018 - 11:48:17



  • HAL Id : hal-01091513, version 1



Jérémie Kellner, Alain Celisse. High-dimensional test for normality. Journées des Statistiques, Jun 2014, Rennes, France. 〈hal-01091513〉



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