Modeling Extreme Events Using Heavy-Tailed Distributions

Mathukumalli, V (2016) Modeling Extreme Events Using Heavy-Tailed Distributions. In: Fusion Methodologies in Crisis Management: Higher Level Fusion and Decision Making. Springer International Publishing, pp. 455-465. ISBN 978-3-319-22526-5

Full text not available from this repository. (Request a copy)


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 so-called stable distributions to fit the set of samples. In most cases, stable distributions are heavy-tailed, 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.

[error in script]
IITH Creators:
IITH CreatorsORCiD
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
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 2156 Statistics for this ePrint Item