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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]

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

Contents

DefinitionEdit

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

 

where the support is   for   and   for  .

 

CharacterizationEdit

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

 ,

again, for   when  , and   when  .

The pdf is a solution of the following differential equation:

 

Special casesEdit

  • 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 stands for the exponentiated generalized Pareto distribution. Unlike GPD, exGPD has the finite moments of all orders and possesses separate interpretations for the scale parameter and the shape parameter, which leads to stable and efficient parameter estimation than using GPD.
  • GPD is similar to the Burr distribution.

Generating generalized Pareto random variablesEdit

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 MixtureEdit

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 alsoEdit

ReferencesEdit

  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. JSTOR 1269343.
  4. ^ Davison, A. C. (1984-09-30). "Modelling Excesses over High Thresholds, with an Application". In de Oliveira, J. Tiago (ed.). 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 readingEdit

  • Pickands, James (1975). "Statistical inference using extreme order statistics". Annals of Statistics. 3: 119–131. doi:10.1214/aos/1176343003.
  • Balkema, A.; De Haan, Laurens (1974). "Residual life time at great age". Annals of Probability. 2 (5): 792–804. doi:10.1214/aop/1176996548.
  • Lee, Seyoon; Kim, J.H.K. (2018). "Exponentiated generalized Pareto distribution:Properties and applications towards extreme value theory". Communications in Statistics - Theory and Methods. 0: 1–25. arXiv:1708.01686. doi:10.1080/03610926.2018.1441418.
  • N. L. Johnson; S. Kotz; N. Balakrishnan (1994). Continuous Univariate Distributions Volume 1, second edition. New York: Wiley. ISBN 978-0-471-58495-7. Chapter 20, Section 12: Generalized Pareto Distributions.
  • Barry C. Arnold (2011). "Chapter 7: Pareto and Generalized Pareto Distributions". In Duangkamon Chotikapanich (ed.). Modeling Distributions and Lorenz Curves. New York: Springer. ISBN 9780387727967.
  • Arnold, B. C.; Laguna, L. (1977). On generalized Pareto distributions with applications to income data. Ames, Iowa: Iowa State University, Department of Economics.

External linksEdit