A discussion of “Estimating the historical and future probabilities of large terrorist events'' by Aaron Clauset and Ryan Woodard
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2013
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Resumo |
The terrorist attacks in the United States on September 11, 2001 appeared to be a harbinger of increased terrorism and violence in the 21st century, bringing terrorism and political violence to the forefront of public discussion. Questions about these events abound, and “Estimating the Historical and Future Probabilities of Large Scale Terrorist Event” [Clauset and Woodard (2013)] asks specifically, “how rare are large scale terrorist events?” and, in general, encourages discussion on the role of quantitative methods in terrorism research and policy and decision-making. Answering the primary question raises two challenges. The first is identify- ing terrorist events. The second is finding a simple yet robust model for rare events that has good explanatory and predictive capabilities. The challenges of identifying terrorist events is acknowledged and addressed by reviewing and using data from two well-known and reputable sources: the Memorial Institute for the Prevention of Terrorism-RAND database (MIPT-RAND) [Memorial Institute for the Prevention of Terrorism] and the Global Terror- ism Database (GTD) [National Consortium for the Study of Terrorism and Responses to Terrorism (START) (2012), LaFree and Dugan (2007)]. Clauset and Woodard (2013) provide a detailed discussion of the limitations of the data and the models used, in the context of the larger issues surrounding terrorism and policy. |
Formato |
application/pdf |
Identificador | |
Publicador |
Institute of Mathematical Statistics |
Relação |
http://eprints.qut.edu.au/68793/1/1401.2303v1.pdf DOI:10.1214/13-AOAS614C White, Gentry (2013) A discussion of “Estimating the historical and future probabilities of large terrorist events'' by Aaron Clauset and Ryan Woodard. The Annals of Applied Statistics, 7(4), pp. 1876-1880. |
Direitos |
Copyright 2013 Institute of Mathematical Statistics |
Fonte |
School of Mathematical Sciences; Science & Engineering Faculty |
Palavras-Chave | #010499 Statistics not elsewhere classified |
Tipo |
Journal Article |