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.
Type de document :
Article dans une revue
Signal Processing, Elsevier, 2018, 〈10.1016/j.sigpro.2018.10.022〉
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Contributeur : Jean-Marc Bardet <>
Soumis le : mercredi 7 novembre 2018 - 16:44:49
Dernière modification le : mardi 19 mars 2019 - 01:23:32
Document(s) archivé(s) le : vendredi 8 février 2019 - 15:24:17


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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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