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Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm

Cho, Haeran and Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2023) Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm. Journal of Time Series Analysis. ISSN 0143-9782

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Identification Number: 10.1111/jtsa.12722

Abstract

We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g. by the long-run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.

Item Type: Article
Official URL: https://onlinelibrary.wiley.com/journal/14679892
Additional Information: © 2023 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 30 Aug 2023 10:00
Last Modified: 19 May 2024 07:06
URI: http://eprints.lse.ac.uk/id/eprint/120085

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