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New normality test in high dimension with kernel methods

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 نشر من قبل Jeremie Kellner
 تاريخ النشر 2014
  مجال البحث الاحصاء الرياضي
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 تأليف Jeremie Kellner




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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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