Segmentation of the mean of heteroscedastic data via cross-validation

Details Segmentation of the mean of heteroscedastic data via cross-validation Segmentation of the mean of heteroscedastic data via cross-validation

2009-02-23

arxiv.org/abs/0902.3977v2

Statistics and Computing (2009) electronic

Statistics

Methodology

Scientific paper

10.1007/s11222-010-9196-x

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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