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Robust semi-parametric multiple change-points detection
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DOI：10.1016/j.sigpro.2018.10.022.png)
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Abstract
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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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Change-points detection
Least-Absolute Value criterion
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