Rare events

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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.).

Contents

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

Natural 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, Jérôme; Balesdent, Mathieu (2015). Estimation of rare event probabilities in complex aerospace and other systems: a practical approach. Woodhead publishing in mechanical engineering. Amsterdam: Elsevier Woodhead Publishing. ISBN   978-0-08-100091-5.
  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 Archived 2015-09-30 at the Wayback Machine
  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. "IISS Books and Publications". IISS.
  18. "ACLED Conflict Index". Armed Conflict Location & Event Data. Archived from the original on 2024-11-09.
  19. "Mid 4.0". Archived from the original on 2014-12-19.
  20. 1 2 "INSCR Data Page".
  21. "RAND Database of Worldwide Terrorism Incidents".
  22. "Search Earthquake Catalog".
  23. "The Flood Observatory". floodobservatory.colorado.edu. Retrieved 2025-02-16.
  24. "| Reports | the National Flood Insurance Program | FloodSmart | NFIPServices".
  25. "FAOSTAT". faostat.fao.org. Retrieved 2025-02-16.
  26. "Eruption Search". Archived from the original on 2013-05-23. Retrieved 2025-02-16.
  27. "EM-DAT - The international disaster database". www.emdat.be. Retrieved 2025-02-16.
  28. "Natural Hazards Data | NCEI".
  29. "National, Regional, and State Level Outpatient Illness and Viral Surveillance".
  30. "World Health Organization (WHO)". Archived from the original on 2009-05-07.
  31. http://aviation-safety.net/database/ [ bare URL ]
  32. "Database of radiological incidents and related events".