English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Detecting changepoints in datasets with many variates is a data sciencechallenge of increasing importance. Motivated by the problem of detectingchanges in the incidence of terrorism from a global terrorism database, wepropose a novel approach to multiple changepoint detection in multivariate timeseries. Our method, which we call SUBSET, is a model-based approach which usesa penalised likelihood to detect changes for a wide class of parametricsettings. We provide theory that guides the choice of penalties to use forSUBSET, and that shows it has high power to detect changes regardless ofwhether only a few variates or many variates change. Empirical results showthat SUBSET out-performs many existing approaches for detecting changes in meanin Gaussian data; additionally, unlike these alternative methods, it can beeasily extended to non-Gaussian settings such as are appropriate for modellingcounts of terrorist events.

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