  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.
