Data-driven semi-parametric detection of multiple changes in long-range dependent processes

Abstract

This paper is devoted to the offline multiple changes detection for long-range dependent processes. The observations are supposed to satisfy a semi-parametric long-range dependent 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. 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.