Gumbel distribution information

Gumbel
	Probability density function
	Cumulative distribution function
Notation
Parameters	location (real); scale (real)
Support
PDF	; where
CDF
Mean	; where is the Euler–Mascheroni constant
Median
Mode
Variance
Skewness
Excess kurtosis
Entropy
MGF
CF

In probability theory and statistics, the Gumbel distribution (also known as the type-I generalized extreme value 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. This article uses the Gumbel distribution to model the distribution of the maximum value. To model the minimum value, use the negative of the original values.

The Gumbel distribution is a particular case of the generalized extreme value distribution (also known as the Fisher–Tippett distribution). It is also known as the log-Weibull 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 Gumbel-distributed 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]

^ Gumbel, E.J. (1935), "Les valeurs extrêmes des distributions statistiques" (PDF), Annales de l'Institut Henri Poincaré, 5 (2): 115–158
^ Gumbel E.J. (1941). "The return period of flood flows". The Annals of Mathematical Statistics, 12, 163–190.

[1] Gumbel, E.J. (1935), "Les valeurs extrêmes des distributions statistiques" (PDF), Annales de l'Institut Henri Poincaré, 5 (2): 115–158

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

Gumbel distribution information

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Gumbel
Probability density function
Cumulative distribution function
Notation	${\text{Gumbel}}(\mu ,\beta )$
Parameters	$\mu ,$ location (real) $\beta >0,$ scale (real)
Support	$x\in \mathbb {R}$
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 the Euler–Mascheroni constant
Median	$\mu -\beta \ln(\ln 2)$
Mode	$\mu$
Variance	${\frac {\pi ^{2}}{6}}\beta ^{2}$
Skewness	${\frac {12{\sqrt {6}}\,\zeta (3)}{\pi ^{3}}}\approx 1.14$
Excess kurtosis	${\frac {12}{5}}$
Entropy	$\ln(\beta )+\gamma +1$
MGF	$\Gamma (1-\beta t)e^{\mu t}$
CF	$\Gamma (1-i\beta t)e^{i\mu t}$