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High-dimensional test for normality
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Document type :
Communication dans un congrès avec actes
Permalink :
http://hdl.handle.net/20.500.12210/29248
Title :
High-dimensional test for normality
Author(s) :
Kellner, Jérémie [Auteur]
Celisse, Alain [Auteur] refId
Conference title :
Journées des Statistiques
City :
Rennes
Country :
France
Start date of the conference :
2014-06-02
Publication date :
2014-06-02
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
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 ...
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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.Show less >
Language :
Anglais
Audience :
Nationale
Popular science :
Non
Administrative institution(s) :
CNRS
Université de Lille
Collections :
  • METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Submission date :
2020-06-08T14:10:37Z
Université de Lille

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