Rare or extreme events are events that occur with low frequency, and often refers to infrequent events that have a widespread effect and which might destabilize systems (for example, stock markets,[1] ocean wave intensity[2] or optical fibers[3] or society[4]). Rare events encompass natural phenomena (major earthquakes, tsunamis, hurricanes, floods, asteroid impacts, solar flares, etc.), anthropogenic hazards (warfare and related forms of violent conflict, acts of terrorism, industrial accidents, financial and commodity market crashes, etc.), as well as phenomena for which natural and anthropogenic factors interact in complex ways (epidemic disease spread, global warming-related changes in climate and weather, etc.).

Overview

Rare or extreme events are discrete occurrences of infrequently observed events. Despite being statistically improbable, such events are plausible insofar as historical instances of the event (or a similar event) have been documented.[5] Scholarly and popular analyses of rare events often focus on those events that could be reasonably expected to have a substantial negative effect on a society—either economically[6] or in terms of human casualties[7] (typically, both). Examples of such events might include an 8.0+ Richter magnitude earthquake, a nuclear incident that kills thousands of people, or a 10%+ single-day change in the value of a stock market index.[8][9][10]

Modeling and analysis

Rare event modeling (REM) refers to efforts to characterize the statistical distribution parameters, generative processes, or dynamics that govern the occurrence of statistically rare events, including but not limited to highly influential natural or human-made catastrophes. Such “modeling” may include a wide range of approaches, including, most notably, statistical models for analyzing historical event data[11][12] and computational software models that attempt to simulate rare event processes and dynamics.[13] REM also encompasses efforts to forecast the occurrence of similar events over some future time horizon, which may be of interest for both scholarly and applied purposes (e.g., risk mitigation and planning).[14] Novel data collection techniques can be used for learning about rare events data.[15]

Relevant data sets

In many cases, rare and catastrophic events can be regarded as extreme-magnitude instances of more mundane phenomena. For example, seismic activity, stock market fluctuations, and acts of organized violence all occur along a continuum of extremity, with more extreme-magnitude cases being statistically more infrequent.[16] Therefore, rather than viewing rare event data as its own class of information, data concerning "rare" events often exists as a subset of data within a broader parent event class (e.g., a seismic activity data set would include instances of extreme earthquakes, as well as data on much lower-intensity seismic events).

The following is a list of data sets focusing on domains that are of broad scholarly and policy interest, and where "rare" (extreme-magnitude) cases may be of particularly keen interest due to their potentially devastating consequences. Descriptions of the data sets are extracted from the source websites or providers.

Conflicts

See also: List of ongoing armed conflicts, List of terrorist incidents, and Political violence

Natural disasters

See also: Lists of disasters

Diseases

Others

See also

References

  1. ^ Sornette, Didier (2017). Why stock markets crash : critical events in complex financial systems. Princeton University Press. ISBN 9781400885091.
  2. ^ Dysthe, Kristian; Krogstad, Harald E.; Müller, Peter (January 2008). "Oceanic Rogue Waves". Annual Review of Fluid Mechanics. 40 (1): 287–310. Bibcode:2008AnRFM..40..287D. doi:10.1146/annurev.fluid.40.111406.102203.
  3. ^ Dudley, John M.; Dias, Frédéric; Erkintalo, Miro; Genty, Goëry (28 September 2014). "Instabilities, breathers and rogue waves in optics". Nature Photonics. 8 (10): 755–764. arXiv:1410.3071. Bibcode:2014NaPho...8..755D. doi:10.1038/nphoton.2014.220. S2CID 53349599.
  4. ^ King, Gary; Zeng, Langche (2001). "Logistic Regression in Rare Events Data". Political Analysis. 9 (2): 137–163. doi:10.1093/oxfordjournals.pan.a004868.
  5. ^ Morio, J., Balesdent, M. (2015). Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems. Elsevier Science. http://store.elsevier.com/product.jsp?isbn=9780081000915&pagename=search
  6. ^ Sanders, D. (2002). The management of losses arising from extreme events. Paper presented at General Insurance Convention. http://www.actuaries.org.uk/research-and-resources/documents/management-losses-arising-extreme-events
  7. ^ Clauset, Aaron; Woodard, Ryan (2013). "Estimating the historical and future probabilities of large terrorist events". The Annals of Applied Statistics. 7 (4): 1838–1865. arXiv:1209.0089. doi:10.1214/12-AOAS614. S2CID 3088917.
  8. ^ Ghil, M.; Yiou, P.; Hallegatte, S.; Malamud, B. D.; Naveau, P.; Soloviev, A.; Friederichs, P.; Keilis-Borok, V.; Kondrashov, D.; Kossobokov, V.; Mestre, O.; Nicolis, C.; Rust, H. W.; Shebalin, P.; Vrac, M.; Witt, A.; Zaliapin, I. (2011). "Extreme events: Dynamics, statistics and prediction". Nonlinear Processes in Geophysics. 18 (3): 295–350. Bibcode:2011NPGeo..18..295G. doi:10.5194/npg-18-295-2011.
  9. ^ Sharma, A. S.; Bunde, A.; Dimri, V.P.; Baker, D.N. (6 May 2013). Extreme events and natural hazards: The complexity perspective. Wiley. ISBN 9781118672235.
  10. ^ Watkins, N. W. (2013). "Bunched black (And grouped grey) swans: Dissipative and non-dissipative models of correlated extreme fluctuations in complex geosystems" (PDF). Geophysical Research Letters. 40 (2): 402–410. Bibcode:2013GeoRL..40..402W. doi:10.1002/grl.50103.
  11. ^ King, Gary; Zeng, Langche (2001). "Logistic Regression in Rare Events Data". Political Analysis. 9 (2): 137–163. doi:10.1093/oxfordjournals.pan.a004868. ISSN 1047-1987. JSTOR 25791637.
  12. ^ King, Gary; Zeng, Langche (2001). "Explaining Rare Events in International Relations". International Organization. 55 (3): 693–715. doi:10.1162/00208180152507597. ISSN 0020-8183. JSTOR 3078661. S2CID 17865688.
  13. ^ Klüppelberg, Claudia (1997). Modelling Extremal Events. doi:10.1007/978-3-642-33483-2. ISBN 978-3-642-08242-9.
  14. ^ Goodwin, Paul; Wright, George (2010). "The limits of forecasting methods in anticipating rare events" (PDF). Technological Forecasting and Social Change. 77 (3): 355–368. doi:10.1016/j.techfore.2009.10.008.
  15. ^ King, Gary; Zeng, Langche (2002-05-30). "Estimating risk and rate levels, ratios and differences in case-control studies". Statistics in Medicine. 21 (10): 1409–1427. doi:10.1002/sim.1032. ISSN 0277-6715. PMID 12185893. S2CID 11387977.
  16. ^ Clauset, Aaron; Shalizi, Cosma Rohilla; Newman, M. E. J. (2009). "Power-Law Distributions in Empirical Data". SIAM Review. 51 (4): 661–703. arXiv:0706.1062. Bibcode:2009SIAMR..51..661C. doi:10.1137/070710111. S2CID 9155618.
  17. ^ https://acd.iiss.org/
  18. ^ http://www.acleddata.com/data/
  19. ^ https://web.archive.org/web/20141219135756/http://www.correlatesofwar.org/COW2%20Data/MIDs/MID40.html
  20. ^ http://www.systemicpeace.org/inscrdata.html
  21. ^ https://www.rand.org/nsrd/projects/terrorism-incidents.html
  22. ^ http://www.systemicpeace.org/inscrdata.html
  23. ^ https://earthquake.usgs.gov/earthquakes/search/
  24. ^ http://floodobservatory.colorado.edu/
  25. ^ http://www.fema.gov/policy-claim-statistics-flood-insurance/policy-claim-statistics-flood-insurance/policy-claim-13
  26. ^ http://faostat.fao.org/
  27. ^ http://www.volcano.si.edu/search_eruption.cfm
  28. ^ http://www.emdat.be/
  29. ^ http://www.ngdc.noaa.gov/hazard/
  30. ^ http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html
  31. ^ [1]
  32. ^ http://aviation-safety.net/database/
  33. ^ http://www.johnstonsarchive.net/nuclear/radevents/
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