Segmentation in the mean of heteroscedastic data via resampling or cross-validation
Celisse , Alain (INRA , Paris (France). UMR 0518 Mathématiques et Informatique Appliquées) | Arlot , Sylvain (Centre National de la Recherche ScientifiqueEcole Normale Supérieure, ParisParis(France). UMR8548 Laboratoire d'informatique de l'école normale supérieure (LIENS)Willow Project-Team, Laboratoire d’Informatique)
This paper tackles the problem of detecting abrupt changes in the mean of a heteroscedastic signal by model selection, without knowledge on the variations of the noise. A new family of change-point detection procedures is proposed, showing that cross-validation methods can be successful in the heteroscedastic framework, whereas most existing procedures are not robust to heteroscedasticity. The robustness to heteroscedasticity of the proposed procedures is supported by an extensive simulation study, together with recent partial theoretical results. An application to Comparative Genomic Hybridization (CGH) data is provided, showing that robustness to heteroscedasticity can indeed be required for their analysis.
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