Predicting political violence using a state-space model

References

• Bell, C., C. Besaw, and M. Frank. 2021. “The Rulers, Elections, and Irregular Governance (REIGN) Dataset.” https://oefdatascience.github.io/REIGN.github.io/
   Google Scholar
• Brandt, P. T., J. R. Freeman, and P. A. Schrodt. 2011. “Real Time, Time Series Forecasting of Inter-and Intra-State Political Conflict.” Conflict Management and Peace Science 28 (1): 41–64. doi:10.1177/0738894210388125.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Cappé, O., E. Moulines, and T. Rydén. 2005. Inference in Hidden Markov Models. New York, USA: Springer.
   Google Scholar
• Chopin, N., and O. Papaspiliopoulos. 2020. An Introduction to Sequential Monte Carlo. Cham, Switzerland: Springer.
   Google Scholar
• Dahlin, J., and F. Lindsten. 2014. “Particle Filter-Based Gaussian Process Optimisation for Parameter Inference.” In Proceedings of the 19th IFAC World Congress, Cape Town, South Africa, August., pp. 8675–8680. Elsevier: Amsterdam.
   Google Scholar
• Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society: Series B (Methodological) 39 (1): 1–38. doi:10.1111/j.2517-6161.1977.tb01600.x.
   Web of Science ®Google Scholar
• Doucet, A., and A. M. Johansen. 2011. “A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later.” In Nonlinear Filtering Handbook, edited by D. Crisan and B. Rozovsky, 656–704. Oxford: Oxford University Press.
   Google Scholar
• Friedland, B. 1986. Control System Design: An Introduction to State-Space Methods. New York, USA: Dover Publications, Inc.
   Google Scholar
• Gleditsch, Nils Petter, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg, and Håvard Strand. 2002. Armed Conflict 1946-2001: A New Dataset. Journal of Peace Research 39(5).
   Google Scholar
• Godsill, S. 2019. “Particle Filtering: The First 25 Years and beyond.” In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 7760–7764. IEEE: Brighton, UK.
   Google Scholar
• Gordon, N. J., D. J. Salmond, and A. F. Smith. 1993. “Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation.” IEE Proceedings F (Radar and Signal Processing), 140: 107–113.
   Google Scholar
• Greene, W. H. 1994. “Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models.” NYU Working Paper No. EC-94-10, New York, USA.
   Google Scholar
• Hamilton, J. D. 1994. “State-Space Models.” In Handbook of econometrics, edited by Griliches, Zvi, Intriligator, Michael D., Engle, R. F., and McFadden, Daniel, Vol. 4, 3039–3080. Amsterdam, Netherlands: North-Holland.
   Google Scholar
• Hegre, H., M. Allansson, M. Basedau, M. Colaresi, M. Croicu, H. Fjelde, Frederick Hoyles, et al. 2019. “Views: A Political Violence Early-Warning System.” Journal of Peace Research 56 (2): 155–174. doi:10.1177/0022343319823860.
   Web of Science ®Google Scholar
• Hegre, H., C. Bell, M. Colaresi, M. Croicu, F. Hoyles, R. Jansen, A. Lindqvist-McGowan, et al. 2021. “ViEWS_2020: Revising and Evaluating the ViEWS Political Violence Early-Warning System.” Journal of Peace Research 58 (3): 599–611.
   Web of Science ®Google Scholar
• Hegre, H., P. Vesco, and M. Colaresi. 2022. “Lessons from an Escalation Prediction Competition.” International Interactions 48 (4).
   Google Scholar
• Jansen, R., H. Hegre, M. Colaresi, and F. Hoyles. 2020. Benchmark Models for the ViEWS Prediction Competition. Uppsala, Sweden: Department of Peace and Conflict Research.
   Google Scholar
• Lindholm, A., and F. Lindsten. 2019. “Learning dynamical systems with particle stochastic approximation em”. arXiv:1806.09548.
   Google Scholar
• Naesseth, C. A., F. Lindsten, and T. B. Schön. 2019a. “Elements of Sequential Monte Carlo.” Foundations and Trends in Machine Learning 12 (3): 307–392.
   Web of Science ®Google Scholar
• Naesseth, C. A., F. Lindsten, and T. B. Schön. 2019b. “High-Dimensional Filtering Using Nested Sequential Monte Carlo.” IEEE Transactions on Signal Processing 67 (16): 4177–4188. doi:10.1109/TSP.2019.2926035.
   Web of Science ®Google Scholar
• Patterson, T. A., L. Thomas, C. Wilcox, O. Ovaskainen, and J. Matthiopoulos. 2008. “State–Space Models of Individual Animal Movement.” Trends in Ecology & Evolution 23 (2): 87–94. doi:10.1016/j.tree.2007.10.009.
   PubMed Web of Science ®Google Scholar
• Perry, C. 2013. “Machine Learning and Conflict Prediction: A Use Case.” Stability: International Journal of Security and Development 2 (3): 56.
   Google Scholar
• Schervish, M. J. 1995. Theory of Statistics. New York, USA: Springer.
   Google Scholar
• Schön, T. B., F. Gustafsson, and P.-J. Nordlund. 2005. “Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models.” IEEE Transactions on Signal Processing 53 (7): 2279–2289. doi:10.1109/TSP.2005.849151.
   Web of Science ®Google Scholar
• Schön, T. B., F. Lindsten, J. Dahlin, J. Wågberg, C. A. Naesseth, A. Svensson, and Liang Dai. 2015. “Sequential Monte Carlo Methods for System Identification.” In Proceedings of the 17th IFAC Symposium on System Identification (SYSID). Beijing, China, 775–786.
   Google Scholar
• Schön, T. B., A. Wills, and B. Ninness. 2011. “System Identification of Nonlinear State-Space Models.” Automatica 47 (1): 39–49. doi:10.1016/j.automatica.2010.10.013.
   Web of Science ®Google Scholar
• Tollefsen, A. F., H. Strand, and H. Buhaug. 2012. “Prio-Grid: A Unified Spatial Data Structure.” Journal of Peace Research 49 (2): 363–374. doi:10.1177/0022343311431287.
   Web of Science ®Google Scholar
• Vesco, P., H. Håvard, M. Colaresi, R. B. Jansen, A. Lo, G. Reisch, and N. B. Weidmann. 2022. “United They Stand: Findings from an Escalation Prediction Competition.” International Interactions 48 (4).
   Google Scholar