Detecting multiple change-points in the mean of gaussian process by model selection

E. Lebarbier

This paper deals with the problem of detecting change-points in the mean of a signal corrupted by an additive Gaussian noise. The number of changes and their position are unknown. From a nonasymptotic point of view, we propose to estimate them with a method based on a penalized least-squares criterion. We choose the penalty function such that the resulting estimator minimizes the quadratic risk according to the results of Birge´ and Massart. This penalty depends on unknown constants and we propose a calibration to obtain an automatic method. The performance of the method is assessed through simulation experiments. An application to real data is shown.

Mots clés: Model selection; Penalized contrast; Detection of change-points

Domaines: Sciences du Vivant [q-bio]

E. Lebarbier. Detecting multiple change-points in the mean of gaussian process by model selection. Signal Processing, 2005, 85 (4), pp.717-736. ⟨10.1016/j.sigpro.2004.11.012⟩. ⟨hal-01588618⟩