Data-driven semi-parametric detection of multiple changes in long-range dependent processes
Statistics Theory
2019-01-01 v2 Statistics Theory
Abstract
This paper is devoted to the offline multiple changes detection for long-range dependence processes. The observations are supposed to satisfy a semi-parametric long-range dependence assumption with distinct memory parameters on each stage. A penalized local Whittle contrast is considered for estimating all the parameters, notably the number of changes. The consistency as well as convergence rates are obtained. Monte-Carlo experiments exhibit the accuracy of the estimators. They also show that the estimation of the number of breaks is improved by using a data-driven slope heuristic procedure of choice of the penalization parameter.
Cite
@article{arxiv.1801.02515,
title = {Data-driven semi-parametric detection of multiple changes in long-range dependent processes},
author = {Jean-Marc Bardet and Abdellatif Guenaizi},
journal= {arXiv preprint arXiv:1801.02515},
year = {2019}
}