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extreme value theory
Extreme value theory or extreme value analysis (EVA) is a branch of statistics dealing with the extreme deviations from the median of probability distributions. It seeks to assess, from a given ordered sample of a given random variable, the probability of events that are more extreme than any previously observed. Extreme value analysis is widely used in many disciplines, such as structural engineering, finance, earth sciences, traffic prediction, and geological engineering. For example, EVA might be used in the field of hydrology to estimate the probability of an unusually large flooding event, such as the 100-year flood. Similarly, for the design of a breakwater, a coastal engineer would seek to estimate the 50-year wave and design the structure accordingly.
== Data analysis ==
Two approaches exist for practical extreme value analysis. The first method relies on deriving block maxima (minima) series as a preliminary step. In many situations it is customary and convenient to extract the annual maxima (minima), generating an "Annual Maxima Series" (AMS). The second method relies on extracting, from a continuous record, the peak values reached for any period during which values exceed a certain threshold (falls below a certain threshold). This method is generally referred to as the "Peak Over Threshold" 〔Leadbetter (1991)〕 method (POT) and can lead to several or no values being extracted in any given year.
For AMS data, the analysis may partly rely on the results of the Fisher–Tippett–Gnedenko theorem, leading to the generalized extreme value distribution being selected for fitting.〔Fisher and Tippett (1928)〕〔Gnedenko (1943)〕 However, in practice, various procedures are applied to select between a wider range of distributions. The theorem here relates to the limiting distributions for the minimum or the maximum of a very large collection of independent random variables from the same arbitrary distribution. Given that the number of relevant random events within a year may be rather limited, it is unsurprising that analyses of observed AMS data often lead to distributions other than the generalized extreme value distribution being selected.〔Embrechts, Klüppelberg, and Mikosch (1997)〕
For POT data, the analysis involves fitting two distributions: one for the number of events in a basic time period and a second for the size of the exceedances. A common assumption for the first is the Poisson distribution, with the generalized Pareto distribution being used for the exceedances. Some further theory needs to be applied in order to derive the distribution of the most extreme value that may be observed in a given period, which may be a target of the analysis. An alternative target may be to estimate the expected costs associated with events occurring in a given period. For POT analyses, a tail-fitting can be based on the Pickands–Balkema–de Haan theorem.〔Pickands (1975)〕〔Balkema and de Haan (1974)〕
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