Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
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
Robust Curvature Extrema Detection Based on New Numerical Derivation
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Change detection in rainfall and temperature patterns over India
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
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
Approximation algorithms for speeding up dynamic programming and denoising aCGH data
Journal of Experimental Algorithmics (JEA)
Computers and Electronics in Agriculture
Exploring the latent segmentation space for the assessment of multiple change-point models
Computational Statistics
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A methodology for model selection based on a penalized contrast is developed. This methodology is applied to the change-point problem, for estimating the number of change points and their location. We aim to complete previous asymptotic results by constructing algorithms that can be used in diverse practical situations. First, we propose an adaptive choice of the penalty function for automatically estimating the dimension of the model, i.e., the number of change points. In a Bayesian framework, we define the posterior distribution of the change-point sequence as a function of the penalized contrast. MCMC procedures are available for sampling this posterior distribution. The parameters of this distribution are estimated with a stochastic version of EM algorithm (SAEM). An application to EEG analysis and some Monte-Carlo experiments illustrate these algorithms.