Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
An Experimental and Theoretical Comparison of Model SelectionMethods
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Using penalized contrasts for the change-point problem
Signal Processing
Nonparametric density estimation by exact leave-p-out cross-validation
Computational Statistics & Data Analysis
Data-driven Calibration of Penalties for Least-Squares Regression
The Journal of Machine Learning Research
A regularized kernel-based approach to unsupervised audio segmentation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Computers in Biology and Medicine
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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 partial theoretical results. An application to Comparative Genomic Hybridization (CGH) data is provided, showing that robustness to heteroscedasticity can indeed be required for their analysis.