Journal
ANNALS OF APPLIED STATISTICS
Volume 7, Issue 4, Pages 2402-2430Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/13-AOAS682
Keywords
Hidden Markov model; self-exciting hurdle model; terrorism; terrorist groups; Colombia; Peru; Indonesia; point process; spurt detection
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Funding
- U.S. Defense Threat Reduction Agency (DTRA) [HDTRA-1-10-1-0086]
- U.S. Defense Advanced Research Projects Agency [W911NF-12-1-0034]
- U.S. National Science Foundation [DMS-12-21888]
- U.S. Air Force Office of Scientific Research (AFOSR) via the MURI at the University of Southern California [FA9550-10-1-0569]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1221888] Funding Source: National Science Foundation
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The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and, in general, tracking it over a period of time. Toward this goal, a d-state hidden Markov model (HMM) that captures the latent states underlying the dynamics of the group and thus its activity profile is developed. The simplest setting of d = 2 corresponds to the case where the dynamics are coarsely quantized as Active and Inactive, respectively. A state estimation strategy that exploits the underlying HMM structure is then developed for spurt detection and tracking. This strategy is shown to track even nonpersistent changes that last only for a short duration at the cost of learning the underlying model. Case studies with real terrorism data from open-source databases are provided to illustrate the performance of the proposed methodology.
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