A computationally efficient, high‐dimensional multiple changepoint procedure with application to glo...
A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence
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Publisher
Oxford: Oxford University Press
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Language
English
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Publisher
Oxford: Oxford University Press
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Scope and Contents
Contents
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 m...
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Full title
A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence
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TN_cdi_proquest_journals_2588774805
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2588774805
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ISSN
0964-1998
E-ISSN
1467-985X
DOI
10.1111/rssa.12695
