High-dimensional test for normality

Kellner, Jérémie; Celisse, Alain

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]

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 ...
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

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

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