Segmentation of the mean of heteroscedastic data via cross-validation

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.

References in corpus (5)

• A survey of cross-validation procedures for model selection
• Consistency of cross validation for comparing regression procedures
• Model selection by resampling penalization
• Gaussian model selection with an unknown variance
• Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

Cited by in corpus (4)

• Optimal cross-validation in density estimation with the $L^2$-loss
• Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases
• Smoothing splines for discontinuous signals
• Degrees-of-freedom penalized piecewise regression