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High-dimensional test for normality


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.
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Dates and versions

hal-01091513 , version 1 (05-12-2014)


  • 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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