Open Access
May 2017 A robust approach for estimating change-points in the mean of an $\operatorname{AR}(1)$ process
S. Chakar, E. Lebarbier, C. Lévy-Leduc, S. Robin
Bernoulli 23(2): 1408-1447 (May 2017). DOI: 10.3150/15-BEJ782


We consider the problem of multiple change-point estimation in the mean of an $\operatorname{AR}(1)$ process. Taking into account the dependence structure does not allow us to use the dynamic programming algorithm, which is the only algorithm giving the optimal solution in the independent case. We propose a robust estimator of the autocorrelation parameter, which is consistent and satisfies a central limit theorem in the Gaussian case. Then, we propose to follow the classical inference approach, by plugging this estimator in the criteria used for change-points estimation. We show that the asymptotic properties of these estimators are the same as those of the classical estimators in the independent framework. The same plug-in approach is then used to approximate the modified BIC and choose the number of segments. This method is implemented in the R package AR1seg and is available from the Comprehensive R Archive Network (CRAN). This package is used in the simulation section in which we show that for finite sample sizes taking into account the dependence structure improves the statistical performance of the change-point estimators and of the selection criterion.


Download Citation

S. Chakar. E. Lebarbier. C. Lévy-Leduc. S. Robin. "A robust approach for estimating change-points in the mean of an $\operatorname{AR}(1)$ process." Bernoulli 23 (2) 1408 - 1447, May 2017.


Received: 1 February 2015; Revised: 1 October 2015; Published: May 2017
First available in Project Euclid: 4 February 2017

zbMATH: 1378.62059
MathSciNet: MR3606770
Digital Object Identifier: 10.3150/15-BEJ782

Keywords: auto-regressive model , Change-points , Model selection , robust estimation of the $\operatorname{AR}(1)$ parameter , time series

Rights: Copyright © 2017 Bernoulli Society for Mathematical Statistics and Probability

Vol.23 • No. 2 • May 2017
Back to Top