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- W2951115937 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." @default.
- W2951115937 created "2019-06-27" @default.
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- W2951115937 date "2011-02-09" @default.
- W2951115937 modified "2023-10-18" @default.
- W2951115937 title "Robust Retrospective Multiple Change-point Estimation for Multivariate Data" @default.
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