Fréchet distribution

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Probability density function
PDF of the Fréchet distribution
Cumulative distribution function
CDF of the Fréchet distribution
Parameters shape.
(Optionally, two more parameters)
scale (default: )
location of minimum (default: )
Ex. kurtosis
Entropy , where is the Euler–Mascheroni constant.
MGF [1] Note: Moment exists if
CF [1]

The Fréchet distribution, also known as inverse Weibull distribution,[2][3] is a special case of the generalized extreme value distribution. It has the cumulative distribution function

where α > 0 is a shape parameter. It can be generalised to include a location parameter m (the minimum) and a scale parameter s > 0 with the cumulative distribution function

Named for Maurice Fréchet who wrote a related paper in 1927,[4] further work was done by Fisher and Tippett in 1928 and by Gumbel in 1958.[5][6]


The single parameter Fréchet with parameter has standardized moment

(with ) defined only for :

where is the Gamma function.

In particular:

  • For the expectation is
  • For the variance is

The quantile of order can be expressed through the inverse of the distribution,


In particular the median is:

The mode of the distribution is

Especially for the 3-parameter Fréchet, the first quartile is and the third quartile

Also the quantiles for the mean and mode are:


Fitted cumulative Fréchet distribution to extreme one-day rainfalls

However, in most hydrological applications, the distribution fitting is via the generalized extreme value distribution as this avoids imposing the assumption that the distribution does not have a lower bound (as required by the Frechet distribution).[citation needed]

Fitted decline curve analysis. Duong model can be thought of as a generalization of the Frechet distribution.
  • In decline curve analysis, a declining pattern the time series data of oil or gas production rate over time for a well can be described by the Fréchet distribution. [8]

  • One test to assess whether a multivariate distribution is asymptotically dependent or independent consists of transforming the data into standard Fréchet margins using the transformation and then mapping from Cartesian to pseudo-polar coordinates . Values of correspond to the extreme data for which at least one component is large while approximately 1 or 0 corresponds to only one component being extreme.

Related distributions[edit]

  • If (Uniform distribution (continuous)) then
  • If then
  • If and then
  • The cumulative distribution function of the Frechet distribution solves the maximum stability postulate equation
  • If then its reciprocal is Weibull-distributed:


See also[edit]


  1. ^ a b Muraleedharan. G, C. Guedes Soares and Cláudia Lucas (2011). "Characteristic and Moment Generating Functions of Generalised Extreme Value Distribution (GEV)". In Linda. L. Wright (Ed.), Sea Level Rise, Coastal Engineering, Shorelines and Tides, Chapter 14, pp. 269–276. Nova Science Publishers. ISBN 978-1-61728-655-1
  2. ^ Khan M.S.; Pasha G.R.; Pasha A.H. (February 2008). "Theoretical Analysis of Inverse Weibull Distribution" (PDF). WSEAS TRANSACTIONS on MATHEMATICS. Vol. 7, no. 2. pp. 30–38.
  3. ^ de Gusmão, FelipeR.S. and Ortega, EdwinM.M. and Cordeiro, GaussM. (2011). "The generalized inverse Weibull distribution". Statistical Papers. Springer-Verlag. 52 (3): 591–619. doi:10.1007/s00362-009-0271-3. ISSN 0932-5026.{{cite journal}}: CS1 maint: uses authors parameter (link)
  4. ^ Fréchet, M. (1927). "Sur la loi de probabilité de l'écart maximum". Ann. Soc. Polon. Math. 6: 93.
  5. ^ Fisher, R. A.; Tippett, L. H. C. (1928). "Limiting forms of the frequency distribution of the largest and smallest member of a sample". Proceedings of the Cambridge Philosophical Society. 24 (2): 180–190. Bibcode:1928PCPS...24..180F. doi:10.1017/S0305004100015681. S2CID 123125823.
  6. ^ Gumbel, E. J. (1958). Statistics of Extremes. New York: Columbia University Press. OCLC 180577.
  7. ^ Coles, Stuart (2001). An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag. ISBN 978-1-85233-459-8.
  8. ^ Lee, Se Yoon; Mallick, Bani (2021). "Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas". Sankhya B. 84: 1–43. doi:10.1007/s13571-020-00245-8.

Further reading[edit]

  • Kotz, S.; Nadarajah, S. (2000) Extreme value distributions: theory and applications, World Scientific. ISBN 1-86094-224-5

External links[edit]