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Pré-Publication, Document De Travail Année : 2011

Robust Retrospective Multiple Change-point Estimation for Multivariate Data

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

hal-00564410 , version 1 (08-02-2011)
hal-00564410 , version 2 (10-02-2011)

Identifiants

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Alexandre Lung-Yut-Fong, Céline Lévy-Leduc, Olivier Cappé. Robust Retrospective Multiple Change-point Estimation for Multivariate Data. 2011. ⟨hal-00564410v1⟩
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