Gumbel
Probability density function

Cumulative distribution function

Parameters

\mu\! location (real)
\beta>0\! scale (real)

Support

x \in (\infty; +\infty)\!

PDF

\frac{1}{\beta}e^{(z+e^{z})}\!
where z=\frac{x\mu}{\beta}\!

CDF

e^{e^{(x\mu)/\beta}}\!

Mean

\mu + \beta\,\gamma\!
where \gamma is Euler’s constant

Median

\mu  \beta\,\ln(\ln(2))\!

Mode

\mu\!

Variance

\frac{\pi^2}{6}\,\beta^2\!

Skewness

\frac{12\sqrt{6}\,\zeta(3)}{\pi^3} \approx 1.14\!

Ex. kurtosis

\frac{12}{5}

Entropy

\ln(\beta)+\gamma+1\!

MGF

\Gamma(1\beta\,t)\, e^{\mu\,t}\!

CF

\Gamma(1i\,\beta\,t)\, e^{i\,\mu\,t}\!

In probability theory and statistics, the Gumbel distribution is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions. This distribution might be used to represent the distribution of the maximum level of a river in a particular year if there was a list of maximum values for the past ten years. It is useful in predicting the chance that an extreme earthquake, flood or other natural disaster will occur. The potential applicability of the Gumbel distribution to represent the distribution of maxima relates to extreme value theory which indicates that it is likely to be useful if the distribution of the underlying sample data is of the normal or exponential type.
The Gumbel distribution is a particular case of the generalized extreme value distribution (also known as the FisherTippett distribution). It is also known as the logWeibull distribution and the double exponential distribution (a term that is alternatively sometimes used to refer to the Laplace distribution). It is related to the Gompertz distribution: when its density is first reflected about the origin and then restricted to the positive half line, a Gompertz function is obtained.
In the latent variable formulation of the multinomial logit model — common in discrete choice theory — the errors of the latent variables follow a Gumbel distribution. This is useful because the difference of two Gumbeldistributed random variables has a logistic distribution.
The Gumbel distribution is named after Emil Julius Gumbel (1891–1966), based on his original papers describing the distribution.^{[1]}^{[2]}
Contents

Properties 1

Standard Gumbel distribution 2

Quantile function and generating Gumbel variates 3

Related distributions 4

Probability paper 5

Application 6

See also 7

References 8

External links 9
Properties
The cumulative distribution function of the Gumbel distribution is

F(x;\mu,\beta) = e^{e^{(x\mu)/\beta}}.\,
The mode is μ, while the median is \mu\beta \ln\left(\ln 2\right), and the mean is given by

\operatorname{E}(X)=\mu+\gamma\beta ,
where \gamma = Euler–Mascheroni constant \approx 0.5772. The standard deviation is \beta \pi/\sqrt{6}.
Standard Gumbel distribution
The standard Gumbel distribution is the case where \mu = 0 and \beta = 1 with cumulative distribution function

F(x) = e^{e^{(x)}}\,
and probability density function

f(x) = e^{(x+e^{x})}.
In this case the mode is 0, the median is \ln(\ln(2)) \approx 0.3665, the mean is \gamma, and the standard deviation is \pi/\sqrt{6} \approx 1.2825.
The cumulants, for n>1, are given by

\kappa_n = (n1)! \zeta(n).
Quantile function and generating Gumbel variates
Since the quantile function(inverse cumulative distribution function), Q(p), of a Gumbel distribution is given by

Q(p)=\mu\beta\ln(\ln(p)),
the variate Q(U) has a Gumbel distribution with parameters \mu and \beta when the random variate U is drawn from the uniform distribution on the interval (0,1).
Related distributions

If X has a Gumbel distribution, then the conditional distribution of Y=X given that Y is positive, or equivalently given that X is negative, has a Gompertz distribution. The cdf G of Y is related to F, the cdf of X, by the formula G(y) = P(Y \le y) = P(X \ge y  X \le 0) = (F(0)F(y))/F(0) for y>0. Consequently the densities are related by g(y) = f(y)/F(0): the Gompertz density is proportional to a reflected Gumbel density, restricted to the positive halfline.^{[3]}

If X is an exponential with mean 1, then log(X) has a standard GumbelDistribution.
Theory related to the generalized multivariate loggamma distribution provides a multivariate version of the Gumbel distribution.
Probability paper
A piece of graph paper that incorporates the Gumbel distribution.
In presoftware times probability paper was used to picture the Gumbel distribution (see illustration). The paper is based on linearization of the cumulative distribution function F :

\ln[\ln(F)] = (x\mu)/\beta
In the paper the horizontal axis is constructed at a double log scale. The vertical axis is linear. By plotting F on the horizontal axis of the paper and the xvariable on the vertical axis, the distribution is represented by a straight line with a slope 1/\beta. When distribution fitting software like CumFreq became available, the task of plotting the distribution was made easier, as is demonstrated in the section below.
Application
Gumbel has shown that the maximum value (or last order statistic) in a sample of a random variable following an exponential distribution approaches the Gumbel distribution closer with increasing sample size.^{[4]}
In hydrology, therefore, the Gumbel distribution is used to analyze such variables as monthly and annual maximum values of daily rainfall and river discharge volumes,^{[5]} and also to describe droughts.^{[6]}
Gumbel has also shown that the estimator r/(n+1) for the probability of an event—where r is the rank number of the observed value in the data series and n is the total number of observations—is an unbiased estimator of the cumulative probability around the mode of the distribution. Therefore, this estimator is often used as a plotting position.
The blue picture illustrates an example of fitting the Gumbel distribution to ranked maximum oneday October rainfalls showing also the 90% confidence band based on the binomial distribution. The rainfall data are represented by the plotting position r/(n+1) as part of the cumulative frequency analysis.
In number theory, the Gumbel distribution approximates the number of terms in a partition of an integer^{[7]} as well as the trendadjusted sizes of record prime gaps and record gaps between prime constellations.^{[8]}
In machine learning, the Gumbel distribution is sometimes employed to generate samples from the categorical distribution for example.^{[9]}
See also
References

^ Gumbel, E.J. (1935), "Les valeurs extrêmes des distributions statistiques" (PDF), Ann. Inst. Henri Poincaré 5 (2): 115–158

^ Gumbel E.J. (1941). "The return period of ﬂood ﬂows". The Annals of Mathematical Statistics, 12, 163–190.

^ Willemse, W.J.; Kaas, R. (2007). "Rational reconstruction of frailtybased mortality models by a generalisation of Gompertz' law of mortality". Insurance: Mathematics and Economics 40 (3): 468.

^ Gumbel, E.J. (1954). Statistical theory of extreme values and some practical applications. Applied Mathematics Series 33 (1st ed.). U.S. Department of Commerce, National Bureau of Standards.

^ Oosterbaan, R.J. (1994). "Chapter 6 Frequency and Regression Analysis". In Ritzema, H.P. Drainage Principles and Applications, Publication 16 (PDF). Wageningen, The Netherlands: International Institute for Land Reclamation and Improvement (ILRI). pp. 175–224.

^ Burke, Eleanor J.; Perry, Richard H.J.; Brown, Simon J. (2010). "An extreme value analysis of UK drought and projections of change in the future". Journal of Hydrology 388: 131.

^ Erdös, Paul; Lehner, Joseph (1941). "The distribution of the number of summands in the partitions of a positive integer". Duke Mathematical Journal 8 (2): 335.

^ Kourbatov, A. (2013). "Maximal gaps between prime ktuples: a statistical approach". Journal of Integer Sequences 16. Article 13.5.2.

^ Adams, Ryan. "The GumbelMax Trick for Discrete Distributions".
External links














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