Robust Retrospective Multiple Change-point Estimation for Multivariate   Data
  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. 
Alexandre Lung-Yut-Fong (LTCI), C\'eline L\'evy-Leduc (LTCI), Olivier   Capp\'e (LTCI)
stat.ME stat.CO