Résumé : We propose a non-parametric statistical procedure for
detecting multiple change-points in multidimensional signals. The
method is based on a test statistic that generalizes the well-known
Kruskal-Wallis procedure to the multivariate setting. The proposed
approach does not require any knowledge about the distribution of the
observations and is parameter-free. It is computationally efficient
thanks to the use of dynamic programming and can also be applied when
the number of change-points is unknown. The method is shown through
simulations to be more robust than alternatives, particularly when
faced with atypical observations (e.g., with outliers), high noise
levels and/or high-dimensional data. We also propose an application to
real sensor equipment data.

Cet exposé se tiendra en salle C20-13, 20ème étage, Université
Paris 1, Centre Pierre Mendès-France, 90 rue de Tolbiac, 75013 Paris
(métro : Olympiades).