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Robust semi-parametric multiple change-point detection

Abstract : This paper is dedicated to define two new multiple change-points detectors in the case of an unknown number of changes in the mean of a signal corrupted by additive noise. Both these methods are based on the Least-Absolute Value (LAV) criterion. Such criterion is well known for improving the robustness of the procedure, especially in the case of outliers or heavy-tailed distributions. The first method is inspired by model selection theory and leads to a data-driven estimator. The second one is an algorithm based on total variation type penalty. These strategies are numerically studied on Monte-Carlo experiments.
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Contributor : Jean-Marc Bardet <>
Submitted on : Wednesday, November 7, 2018 - 4:44:49 PM
Last modification on : Saturday, April 11, 2020 - 2:04:33 AM
Document(s) archivé(s) le : Friday, February 8, 2019 - 3:24:17 PM


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Jean-Marc Bardet, Charlotte Dion. Robust semi-parametric multiple change-point detection. Signal Processing, Elsevier, 2018, ⟨10.1016/j.sigpro.2018.10.022⟩. ⟨hal-01846029v2⟩



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