LPP - Laboratoire Paul Painlevé - UMR 8524, Inria Lille - Nord Europe, 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
https://hal.inria.fr/hal-01091513
Contributeur : Jérémie Kellner
<>
Soumis le : vendredi 5 décembre 2014 - 15:19:46
Dernière modification le : jeudi 12 avril 2018 - 11:08:15
