Titre :

Segmentation of the mean of heteroscedastic data via cross-validation

Auteur(s) :

Arlot, Sylvain [Auteur]
Laboratoire d'informatique de l'école normale supérieure [LIENS]
Celisse, Alain [Auteur correspondant]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Titre de la revue :

Statistics and Computing

Pagination :

electronic

Éditeur :

Springer Verlag (Germany)

Date de publication :

2010-07-08

ISSN :

0960-3174

Mot(s)-clé(s) en anglais :

leave-p-out
CGH profile.
heteroscedastic data
CGH profile
Change-point detection
segmentation
resampling
cross-validation

Discipline(s) HAL :

Statistiques [stat]/Méthodologie [stat.ME]
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]

Résumé en anglais : [en]

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 ...
Lire la suite >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 theoretical results. An application to Comparative Genomic Hybridization (CGH) data is provided, showing that robustness to heteroscedasticity can indeed be required for their analysis.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

DETECT: Nouvelles approches statistiques pour la vision artificielle et la bioinformatique

Commentaire :

Published in Statistics and Computing. DOI: 10.1007/s11222-010-9196-x

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL