A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence
| Autor: | S. O. Tickle, Paul Fearnhead, Idris A. Eckley |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Economics and Econometrics Multivariate statistics Computer science Gaussian 0211 other engineering and technologies 02 engineering and technology High dimensional computer.software_genre Statistics - Applications 01 natural sciences Methodology (stat.ME) 010104 statistics & probability symbols.namesake Applications (stat.AP) 0101 mathematics Statistics - Methodology Incidence (geometry) Parametric statistics 021110 strategic defence & security studies Class (computer programming) Series (mathematics) 16. Peace & justice Global terrorism symbols Data mining Statistics Probability and Uncertainty computer Social Sciences (miscellaneous) |
| Zdroj: | Tickle, S O, Eckley, I & Fearnhead, P 2021, ' A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence ', Journal of the Royal Statistical Society. Series A: Statistics in Society, vol. 184, no. 4, pp. 1303-1325 . https://doi.org/10.1111/rssa.12695 Lancaster University-Pure |
| DOI: | 10.1111/rssa.12695 |
| Popis: | Detecting changepoints in data sets 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. |
| Databáze: | OpenAIRE |
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