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**Hidden Markov models for the activity profile of terrorist groups.**
*(English)*
Zbl 1283.62244

Summary: 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 data bases are provided to illustrate the performance of the proposed methodology.

### MSC:

62P99 | Applications of statistics |

62M99 | Inference from stochastic processes |

91F10 | History, political science |

65C60 | Computational problems in statistics (MSC2010) |

### Keywords:

self-exciting hurdle model; terrorism; Colombia; Peru; Indonesia; point process; spurt detection
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\textit{V. Raghavan} et al., Ann. Appl. Stat. 7, No. 4, 2402--2430 (2013; Zbl 1283.62244)

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