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arXiv · 1801.02515

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

Jean-Marc Bardet·Abdellatif Guenaizi
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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.

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Jean-Marc Bardet, Abdellatif Guenaizi. 2018-01-08. Data-driven semi-parametric detection of multiple changes in long-range dependent processes. https://arxiv.org/abs/1801.02515

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