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2020 Data-driven semi-parametric detection of multiple changes in long-range dependent processes
Jean-Marc Bardet, Abdellatif Guenaizi
Electron. J. Statist. 14(2): 3606-3643 (2020). DOI: 10.1214/20-EJS1757

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.

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Jean-Marc Bardet. Abdellatif Guenaizi. "Data-driven semi-parametric detection of multiple changes in long-range dependent processes." Electron. J. Statist. 14 (2) 3606 - 3643, 2020. https://doi.org/10.1214/20-EJS1757

Information

Received: 1 October 2019; Published: 2020
First available in Project Euclid: 2 October 2020

zbMATH: 07270272
MathSciNet: MR4156830
Digital Object Identifier: 10.1214/20-EJS1757

Subjects:
Primary: 62G05, 62G20
Secondary: 62M05

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Vol.14 • No. 2 • 2020
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