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Raghavan, Vasanthan; Galstyan, Aram; Tartakovsky, Alexander G. (2012)
Publisher: The Institute of Mathematical Statistics
Languages: English
Types: Article
Subjects: Computer Science - Social and Information Networks, self-exciting hurdle model, Physics - Physics and Society, Peru, Indonesia, Statistics - Applications, terrorist groups, spurt detection, Physics - Data Analysis, Statistics and Probability, point process, terrorism, Hidden Markov model, Colombia
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Baddeley, A., Moller, J. and Waagepetersen, R. (2000). Non- and semi-parametric estimation of interaction in inhomogenous point patterns. Statistica Neerlandica 54 329-350.
    • Beittel, J. S. (2010). Colombia: Issues for the Congress. Congressional Research Service Report for the U.S. Congress. Available: [Online]. http://www.fas.org/sgp/crs/row/RL32250.pdf.
    • Belasco, A. (2006). The cost of Iraq, Afghanistan, and other global war on terror operations since 9/11. Congressional Research Service Report for the U.S. Congress. Available: [Online]. http://fpc.state.gov/documents/organization/68791.pdf.
    • Bilmes, J. A. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models Technical Report, International Computer Science Institute, Berkeley CA.
    • Clauset, A. and Gleditsch, K. S. (2012). The developmental dynamics of terrorist organizations. Submitted to American Journal of Political Science.
    • Clauset, A., Young, M. and Gleditsch, K. S. (2007). On the frequency of severe terrorist events. Journal of Conflict Resolution 51 58-87.
    • Cox, D. R. and Isham, V. (1980). Point Processes, 1st ed. Chapman and Hall.
    • Cragin, K. and Daly, S. A. (2004). The Dynamic Terrorist Threat: An Assessment of Group Motivations and Capabilities in a Changing World. RAND Corporation, Santa Monica, CA.
    • Cressie, N. A. C. (1991). Statistics for Spatial Data, 1st ed. Wiley, NY.
    • Diggle, P. J. (2003). Statistical Analysis of Spatial Point Patterns, 2nd ed. Edward Arnold, London.
    • Dixon, P. M. (2002). Ripley's K function. in Encyclopedia of Environmetrics, (A. H. El-Shaarawi and W. W. Piegorsch, Eds.), Wiley, Chichester 2 1796-1803.
    • Dugan, L., LaFree, G. and Piquero, A. (2005). Testing a rational choice model of airline hijackings. Criminology 43 1031-1066.
    • Durbin, J. (1973). Distribution Theory for Tests Based on the Sample Distribution Function, Volume 9 of CBMS-NSF Regional Conference Series in Applied Mathematics. SIAM Publishers, Philadelphia.
    • Enders, W. and Sandler, T. (1993). The effectiveness of antiterrorism policies: A vector autoregressionintervention analysis. The American Political Science Review 87 829-844.
    • Enders, W. and Sandler, T. (2000). Is transnational terrorism becoming more threatening? A time-series investigation. Journal of Conflict Resolution 44 307-332.
    • Haugaard, L., Isacson, A. and Olson, J. (2005). Erasing the lines: Trends in U.S. military programs with Latin America Technical Report, Center for International Policy.
    • Hawkes, A. G. (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika 58 83-90.
    • ITERATE, International Terrorism: Attributes of Terrorist Events. Available: [Online]. http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/07947.
    • Juang, B. H. and Rabiner, L. R. (1990). The segmental K-means algorithm for estimating parameters of hidden Markov models. IEEE Transactions on Acoustics, Speech and Signal Processing 38 1639-1641.
    • Kim, H. (2011). Spatio-temporal point process models for the spread of avian influenza virus (H5N1), Technical Report, University of California, Berkeley. Ph.D. dissertation.
    • LaFree, G. and Dugan, L. (2007). Introducing the Global Terrorism Database. Terrorism and Political Violence 19 181-204.
    • LaFree, G. and Dugan, L. (2009). Tracking global terrorism trends, 1970 - 2004. Chapter 3 of To Protect and To Serve: Policing in an Age of Terrorism, (D. Weisburd et al., Eds.), Springer Science 43-80.
    • LaFree, G., Morris, N. A. and Dugan, L. (2010). Cross-national patterns of terrorism, comparing trajectories for total, attributed and fatal attacks, 1970-2006. British Journal of Criminology 50 622- 649.
    • Lewis, E., Mohler, G. O., Brantingham, P. J. and Bertozzi, A. (2011). Self-exciting point process models of civilian deaths in Iraq. Security Journal. Available: [Online]. http://dx.doi.org/10.1057/sj.2011.21.
    • Lindberg, M. (2010). Factors contributing to the strength and resilience of terrorist groups. Grupo de Estudios Estrategicos (GEES) Publication.
    • Midlarsky, M. I. (1978). Analyzing diffusion and contagion effects: The urban disorders of the 1960s. The American Political Science Review 72 996-1008.
    • Midlarsky, M. I., Crenshaw, M. and Yoshida, F. (1980). Why violence spreads: The contagion of international terrorism. International Studies Quarterly 24 262-298.
    • Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P. and Tita, G. E. (2011). Selfexciting point process modeling of crime. Journal of the American Statistical Association 106 100-108.
    • Mueller, J. and Stewart, M. G. (2011). Terrorism, Security, and Money: Balancing the Risks, Benefits, and Costs of Homeland Security. Oxford Univ. Press, NY and London.
    • Ogata, Y. (1988). Statistical models for earthquake occurrences and residual analysis for point processes. Journal of the American Statistical Association 83 9-27.
    • Ogata, Y. (1998). Space-time point process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics 50 379-402.
    • Porter, M. D. and White, G. (2012). Self-exciting hurdle models for terrorist activity. Annals of Applied Statistics 6 106-124.
    • Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77 257-286.
    • Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics 1 239-250.
    • Santos, D. N. (2011). What constitutes terrorist network resiliency? Small Wars Journal 7.
    • Seshadri, V., Csorgo, M. and Stephens, M. A. (1969). Tests for the exponential distribution using Kolmogorov-type statistics. Journal of the Royal Statistical Society, Series B (Methodological) 31 499- 509.
    • Srivastava, M. S. and Wu, Y. H. (1997). Evaluation of optimum weights and average run lengths in EWMA control schemes. Communications in Statistics - Theory and Methods 26 1253-1267.
    • Tartakovsky, A. G. and Veeravalli, V. V. (2005). General asymptotic Bayesian theory of quickest change detection. SIAM Theory of Probability and its Applications 49 458-497.
    • Teerapabolarn, K. (2012). A pointwise approximation of generalized binomial by Poisson distribution. Applied Mathematical Sciences 6 1095-1104.
    • Veen, A. and Schoenberg, F. P. (2006). Assessing spatial point process models using weighted Kfunctions. in Case Studies in Spatial Point Process Modeling, (A. Baddeley et al., Eds.), Lecture Notes in Statistics, Springer, NY 185 293-306.
    • World Drug Report, (2010). Technical Report, United Nations Office on Drugs and Crime.
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