Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
An application of MCMC methods for the multiple change-points problem
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Data-driven neighborhood selection of a Gaussian field
Computational Statistics & Data Analysis
Joint segmentation of multivariate Gaussian processes using mixed linear models
Computational Statistics & Data Analysis
Segmentation of the mean of heteroscedastic data via cross-validation
Statistics and Computing
Slope heuristics: overview and implementation
Statistics and Computing
Computers and Electronics in Agriculture
Exploring the latent segmentation space for the assessment of multiple change-point models
Computational Statistics
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This paper deals with the problem of detecting change-points in the mean of a signal corrupted by an additive Gaussian noise. The number of changes and their position are unknown. From a nonasymptotic point of view, we propose to estimate them with a method based on a penalized least-squares criterion. We choose the penalty function such that the resulting estimator minimizes the quadratic risk according to the results of Birgé and Massart. This penalty depends on unknown constants and we propose a calibration to obtain an automatic method. The performance of the method is assessed through simulation experiments. An application to real data is shown.