Generalized Pareto distribution

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Generalized Pareto distribution
Probability density function
Gpdpdf
GPD distribution functions for and different values of and
Cumulative distribution function
Gpdcdf
Parameters

location (real)
scale (real)

shape (real)
Support


PDF


where
CDF
Mean
Median
Mode
Variance
Skewness
Ex. kurtosis
Entropy
MGF
CF

In statistics, the generalized Pareto distribution (GPD) is a family of continuous probability distributions. It is often used to model the tails of another distribution. It is specified by three parameters: location , scale , and shape .[1][2] Sometimes it is specified by only scale and shape[3] and sometimes only by its shape parameter. Some references give the shape parameter as .[4]

Definition[edit]

The standard cumulative distribution function (cdf) of the GPD is defined by[5]

where the support is for and for .

Characterization[edit]

The related location-scale family of distributions is obtained by replacing the argument z by and adjusting the support accordingly: The cumulative distribution function is

for when , and when , where , , and .

The probability density function (pdf) is

,

or equivalently

,

again, for when , and when .

The pdf is a solution of the following differential equation:

Special cases[edit]

  • If the shape and location are both zero, the GPD is equivalent to the exponential distribution.
  • With shape and location , the GPD is equivalent to the Pareto distribution with scale and shape .
  • If , , , then , , , where exGPD is the exponentiated generalized Pareto distribution.
  • GPD is quite similar to the Burr distribution.

Generating generalized Pareto random variables[edit]

If U is uniformly distributed on (0, 1], then

and

Both formulas are obtained by inversion of the cdf.

In Matlab Statistics Toolbox, you can easily use "gprnd" command to generate generalized Pareto random numbers.

GPD as an Exponential-Gamma Mixture[edit]

A GPD random variable can also be expressed as an exponential random variable, with a Gamma distributed rate parameter.

and

then

Notice however, that since the parameters for the Gamma distribution must be greater than zero, we obtain the additional restrictions that: must be positive.

See also[edit]

References[edit]

  1. ^ Coles, Stuart (2001-12-12). An Introduction to Statistical Modeling of Extreme Values. Springer. p. 75. ISBN 9781852334598. 
  2. ^ Dargahi-Noubary, G. R. (1989). "On tail estimation: An improved method". Mathematical Geology. 21 (8): 829–842. doi:10.1007/BF00894450. 
  3. ^ Hosking, J. R. M.; Wallis, J. R. (1987). "Parameter and Quantile Estimation for the Generalized Pareto Distribution". Technometrics. 29 (3): 339–349. doi:10.2307/1269343. 
  4. ^ Davison, A. C. (1984-09-30). "Modelling Excesses over High Thresholds, with an Application". In de Oliveira, J. Tiago. Statistical Extremes and Applications. Kluwer. p. 462. ISBN 9789027718044. 
  5. ^ Embrechts, Paul; Klüppelberg, Claudia; Mikosch, Thomas (1997-01-01). Modelling extremal events for insurance and finance. p. 162. ISBN 9783540609315. 

Further reading[edit]

External links[edit]