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hal-00363627, version 2

Segmentation of the mean of heteroscedastic data via cross-validation

Sylvain Arlot () 1, Alain Celisse (Author to contact preferably) 2

Statistics and Computing (2010) electronic

Abstract: 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.

  • 1:  Laboratoire d'informatique de l'école normale supérieure (LIENS)
  • CNRS : UMR8548 – Ecole normale supérieure de Paris - ENS Paris
  • 2:  Mathématiques et Informatique Appliquées (MIA)
  • Institut national de la recherche agronomique (INRA) : UMR0518 – AgroParisTech
  • Domain : Statistics/Methodology
    Mathematics/Statistics
    Statistics/Statistics Theory
  • Keywords : Change-point detection – segmentation – resampling – cross-validation – leave-p-out – heteroscedastic data – CGH profile.
  • Comment : Published in Statistics and Computing. DOI: 10.1007/s11222-010-9196-x
  • Available versions :  v1 (2009-02-23) v2 (2009-04-08)
 
  • hal-00363627, version 2
  • http://hal.archives-ouvertes.fr/hal-00363627
  • oai:hal.archives-ouvertes.fr:hal-00363627
  • From: Alain Celisse <>
  • Submitted on: Wednesday, 8 April 2009 15:24:04
  • Updated on: Wednesday, 12 September 2012 16:35:06