Robust Retrospective Multiple Change-point Estimation for Multivariate Data

Alexandre Lung-Yut-Fong (1) , Céline Lévy-Leduc (1) , Olivier Cappé (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information

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 distributions (e.g., with outliers), high noise levels and/or high-dimensional data.

Mots clés: Change-point estimation; multivariate data; Kruskal-Wallis test; robust statistics; joint segmentation

Domaines: Méthodologie [stat.ME]; Calcul [stat.CO]

Alexandre Lung-Yut-Fong, Céline Lévy-Leduc, Olivier Cappé. Robust Retrospective Multiple Change-point Estimation for Multivariate Data. Statistical Signal Processing Workshop, Jun 2011, Nice, France. pp.405--408, ⟨10.1109/SSP.2011.5967716⟩. ⟨hal-00564410v2⟩