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Homogeneity and change-point detection tests for multivariate data using rank statistics| old_uid | 11890 |
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| title | Homogeneity and change-point detection tests for multivariate data using rank statistics |
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| start_date | 2012/11/30 |
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| schedule | 11h |
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| online | no |
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| location_info | 21e étage |
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| summary | 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 observations (e.g., with outliers), high noise levels and/or high-dimensional data. We also propose an application to real sensor equipment data. |
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| responsibles | Bardet, Cottrell |
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