Aller au contenu. | Aller à la navigation

Outils personnels

Navigation

UMR 5672

logo de l'ENS de Lyon
logo du CNRS
Vous êtes ici : Accueil / Séminaires / Machine Learning and Signal Processing / Multivariate Normality Test for the detection of Weak Tremors

Multivariate Normality Test for the detection of Weak Tremors

Sara El Bouch (PhD Student, GIPSA-lab, Grenoble ; encadrants : Olivier Michel et Pierre Comon)
Quand ? Le 21/06/2022,
de 13:00 à 14:00
Participants Sara El Bouch
Ajouter un événement au calendrier vCal
iCal

Title : Multivariate Normality Test for the detection of Weak Tremors

Asbtract :   In this talk, I will first present a procedure for testing that a multivariate colored stationary time-series is Gaussian. This test is based on the multivariate definition of the Kurtosis initially proposed by Mardia (1970). The complete derivation of the test statistic's distribution under the null hypothesis of Gaussianity is given for the bivariate case and the general multivariate case will be handled using bivariate random projections. I will then proceed to a practical implementation of the normality test, and a study of its performance will be carried for various scenarios on both synthetic data (using colored copulas) and real seismic data.

More information : https://www.gipsa-lab.grenoble-inp.fr/page_pro.php?vid=3524

Exposé en salle M7 101 (ENS de Lyon, site Monod, 1er étage côté Recherche au M7)