PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Segmentation of the mean of heteroscedastic data via cross-validation
Sylvain Arlot and Alain Celisse
Statistics and Computing pp. 1-20, 2010. ISSN 0960-3174


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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7657
Deposited By:Sylvain Arlot
Deposited On:17 March 2011