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  • 2011.03599

    Rights statement: This is the peer reviewed version of the following article: S. O. Tickle, I. A. Eckley, P. Fearnhead (2021), A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. Journal of the Royal Statistical Society: Statistics in society: Series A. doi: 10.1111/rssa.12695 which has been published in final form at https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12695 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 2.33 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

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A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
<mark>Journal publication date</mark>31/10/2021
<mark>Journal</mark>Journal of the Royal Statistical Society: Series A Statistics in Society
Issue number4
Volume184
Number of pages23
Pages (from-to)1303-1325
Publication StatusPublished
Early online date4/08/21
<mark>Original language</mark>English

Abstract

Detecting changepoints in datasets with many variates is a data science challenge of increasing importance. Motivated by the problem of detecting changes in the incidence of terrorism from a global terrorism database, we propose a novel approach to multiple changepoint detection in multivariate time series. Our method, which we call SUBSET, is a model-based approach which uses a penalised likelihood to detect changes for a wide class of parametric settings. We provide theory that guides the choice of penalties to use for SUBSET, and that shows it has high power to detect changes regardless of whether only a few variates or many variates change. Empirical results show that SUBSET out-performs many existing approaches for detecting changes in mean in Gaussian data; additionally, unlike these alternative methods, it can be easily extended to non-Gaussian settings such as are appropriate for modelling counts of terrorist events.

Bibliographic note

This is the peer reviewed version of the following article: S. O. Tickle, I. A. Eckley, P. Fearnhead (2021), A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. Journal of the Royal Statistical Society: Statistics in society: Series A. doi: 10.1111/rssa.12695 which has been published in final form at https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12695 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.