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Preprints, Working Papers, ... Year : 2014

New normality test in high dimension with kernel methods

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.
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Dates and versions

hal-00977839 , version 1 (11-04-2014)

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Jérémie Kellner, Alain Celisse. New normality test in high dimension with kernel methods. 2014. ⟨hal-00977839⟩
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