Robust semi-parametric multiple change-point detection

Jean-Marc Bardet (1) , Charlotte Dion (2)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation

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.

Mots clés: Change-points detection; Least-Absolute Value criterion

Domaines: Statistiques [math.ST]

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