1 |
W. H. Bakun et al., Implications for prediction and hazard assessment from the 2004 parkfield earthquake, Nature 437 (2005), 969-974.
DOI
|
2 |
J. A. Tenreiro Machado, C. M. A. Pinto, and A. Mendes Lopes, Power law and entropy analysis of catastrophic phenomena, Math. Prob. Eng. 2013 (2013), 1-10.
|
3 |
H. Mata-Lima et al., Impactos dos desastres naturais nos sistemas ambiental e socioeconomico: O que faz a diferenca?, Amb. Soc. 16 (2013), 45-64.
DOI
|
4 |
M. Robin, Hogarth, Cognitive processes and the assessment of subjective probability distributions, J. Am. Stat. Assoc. 70 (1975), 271-289.
DOI
|
5 |
R. Caruso and A. Locatelli, Understanding Terrorism: A Socio-Economic Perspective, vol. 22, Emerald Publishing Group, Bingley, UK, 2014.
|
6 |
X. Wang et al., Investigative visual analysis of global terrorism, Comp. Graphics Forum 27 (2008), 919-926.
DOI
|
7 |
G. Lafree, Using open source data to counter common myths about terrorism, in Criminologists on Terrorism and Homeland Security, Cambridge University Press, Cambridge, UK, 2011, pp. 411-442.
|
8 |
START, Codebook: Inclusion criteria and variables, J. Educ. Stat. (2018), 453.
|
9 |
G. LaFree and L. Dugan, Introducing the global terrorism database, Terrorism Polit. Violence 19 (2007), 181-204.
DOI
|
10 |
Z. Li et al. Terrorist group behavior prediction by wavelet transform-based pattern recognition, Dis. Dynam. Nat. Soc. 2018 (2018), 1-16.
|
11 |
G. LaFree and J. D. Freilich, Editor's introduction: Quantitative approaches to the study of terrorism, J. Quant. Criminol. 28 (2012), 1-5.
DOI
|
12 |
K. F. Widaman and R. P. McDonald, Factor analysis and related methods, J. Educ. Stat. 12 (1987), 308-313.
|
13 |
E. L. Huamani, A. Mantari, and A. Roman-Gonzalez, Machine learning techniques to visualize and predict terrorist attacks worldwide using the global terrorism database, Int. J. Advan. Comp. Sci. Appl. 11 (2020), 563-570.
|
14 |
Q. I. U. Lingfeng et al., Study on prediction of global terrorist attacks based on machine learning and fragile states index, J. Catastrophol. 34 (2019), 211-214.
|
15 |
C. Verma, S. Malhotra, and V. Verma, Predictive modeling of terrorist attacks using machine learning, Int. J. Pure Appl. Math. 119 (2018), no. 15, 49-60.
|
16 |
B. Price, A first course in factor analysis, Technometrics 35 (1993), 453.
DOI
|
17 |
J. G. Liu and P. J. Mason, Principal component analysis, in Essential Image Processing and GIS for Remote Sensing, Wiley, London, UK, 2009, pp. 77-90.
|
18 |
L. A. Pasa et al., A contribution to the study of ensemble of self-organizing maps, Math. Prob. Eng. 2015 (2015), 1-10.
|
19 |
H. F. Kaiser, The varimax criterion for analytic rotation in factor analysis, Psychometrika 23 (1958), 187-200.
DOI
|
20 |
K. Kis-Katos, H. Liebert, and G. G. Schulze, On the origin of domestic and international terrorism, Eur. J. Polit. Econ. 27 (2011), S17-S36.
DOI
|
21 |
W. Enders, T. Sandler, and K. Hartley. Chapter 26 terrorism: An empirical analysis, in Handbook of Defense Economics, vol. 2, Elsevier, North Holland, Netherlands, 2007, pp. 815-866.
|