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Poster
Catching Change-points with Lasso
Zaid Harchaoui · Céline Lévy-Leduc
We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise. Our approach consists in reframing this task in a variable selection context. We use a penalized least-squares criterion with a l1-type penalty for this purpose. We prove that, in an appropriate asymptotic framework, this method provides consistent estimators of the change-points. Then, we explain how to implement this method in practice by combining the LAR algorithm and a reduced version of the dynamic programming algorithm and we apply it to synthetic and real data.
Author Information
Zaid Harchaoui (University of Washington)
Céline Lévy-Leduc (GET/Telecom Paris)
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