High-dimensional test for normality
Document type :
Communication dans un congrès avec actes
Title :
High-dimensional test for normality
Author(s) :
Kellner, Jérémie [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Celisse, Alain [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Celisse, Alain [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Conference title :
Journées des Statistiques
City :
Rennes
Country :
France
Start date of the conference :
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 ...
Show more >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 >
Show more >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
Peer reviewed article :
Oui
Audience :
Nationale
Popular science :
Non
Collections :
Source :
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