Robust Retrospective Multiple Change-point Estimation for Multivariate Data
Abstract
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.
Cite
@article{arxiv.1102.1796,
title = {Robust Retrospective Multiple Change-point Estimation for Multivariate Data},
author = {Alexandre Lung-Yut-Fong and Céline Lévy-Leduc and Olivier Cappé},
journal= {arXiv preprint arXiv:1102.1796},
year = {2011}
}
Comments
submitted to IEEE Workshop on Statistical Signal Processing 2011