Modeling Extreme Events Using HeavyTailed Distributions
Mathukumalli, V (2016) Modeling Extreme Events Using HeavyTailed Distributions. In: Fusion Methodologies in Crisis Management: Higher Level Fusion and Decision Making. Springer International Publishing, pp. 455465. ISBN 9783319225265
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Typically, in constructing a model for a random variable, one utilizes available samples to construct an empirical distribution function, which can then be used to estimate the probability that the random variable would exceed a prespecified threshold. However, in modeling extreme events, the threshold is often in excess of the largest sampled value observed thus far. In such cases, the use of empirical distributions would lead to the absurd conclusion that the random variable would never exceed the threshold. Therefore it becomes imperative to fit the observed samples with some appropriate distribution. For reasons explained in the paper, it is desirable to use the socalled stable distributions to fit the set of samples. In most cases, stable distributions are heavytailed, in that they do not have finite variance (and may not even have finite mean). However, they often do a very good job of fitting the data. This is illustrated in this paper via examples from various application areas such as finance and weather.
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Item Type:  Book Section  
Subjects:  Others > Electricity Others > Engineering technology Others > Information sciences 

Divisions:  Department of Electrical Engineering  
Depositing User:  Team Library  
Date Deposited:  27 Jan 2016 04:38  
Last Modified:  27 Jan 2016 04:38  
URI:  http://raiith.iith.ac.in/id/eprint/2156  
Publisher URL:  http://dx.doi.org/10.1007/9783319225272_21  
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