Gumbel distribution

Gumbel

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

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]

Definitions

The cumulative distribution function of the Gumbel distribution is

${\displaystyle F(x;\mu ,\beta )=e^{-e^{-(x-\mu )/\beta }}\,}$

Standard Gumbel distribution

The standard Gumbel distribution is the case where ${\displaystyle \mu =0}$ and ${\displaystyle \beta =1}$ with cumulative distribution function

${\displaystyle F(x)=e^{-e^{(-x)}}\,}$

and probability density function

${\displaystyle f(x)=e^{-(x+e^{-x})}.}$

In this case the mode is 0, the median is ${\displaystyle -\ln(\ln(2))\approx 0.3665}$, the mean is ${\displaystyle \gamma \approx 0.5772}$ (the Euler–Mascheroni constant), and the standard deviation is ${\displaystyle \pi /{\sqrt {6}}\approx 1.2825.}$

The cumulants, for n > 1, are given by

${\displaystyle \kappa _{n}=(n-1)!\zeta (n).}$

Properties

The mode is μ, while the median is ${\displaystyle \mu -\beta \ln \left(\ln 2\right),}$ and the mean is given by

${\displaystyle \operatorname {E} (X)=\mu +\gamma \beta }$,

where ${\displaystyle \gamma }$ is the Euler–Mascheroni constant.

The standard deviation ${\displaystyle \sigma }$ is ${\displaystyle \beta \pi /{\sqrt {6}}}$ hence ${\displaystyle \beta =\sigma {\sqrt {6}}/\pi \approx 0.78\sigma .}$ ^[3]

At the mode, where ${\displaystyle x=\mu }$, the value of ${\displaystyle F(x;\mu ,\beta )}$ becomes ${\displaystyle e^{-1}\approx 0.37}$, irrespective of the value of ${\displaystyle \beta .}$

If ${\displaystyle G_{1},...,G_{k}}$ are iid Gumbel random variables with parameters ${\displaystyle (\mu ,\beta )}$ then ${\displaystyle \max\{G_{1},...,G_{k}\}}$ is also a Gumbel random variable with parameters ${\displaystyle (\mu +\beta \ln k,\beta )}$.

If ${\displaystyle G_{1},G_{2},...}$ are iid random variables such that ${\displaystyle \max\{G_{1},...,G_{k}\}-\beta \ln k}$ has the same distribution as ${\displaystyle G_{1}}$ for all natural numbers ${\displaystyle k}$, then ${\displaystyle G_{1}}$ is necessarily Gumbel distributed with scale parameter ${\displaystyle \beta }$ (actually it suffices to consider just two distinct values of k>1 which are coprime).

Related distributions

• If ${\displaystyle X}$ 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 ${\displaystyle G(y)=P(Y\leq y)=P(X\geq -y\mid X\leq 0)=(F(0)-F(-y))/F(0)}$ for y > 0. Consequently, the densities are related by ${\displaystyle g(y)=f(-y)/F(0)}$: the Gompertz density is proportional to a reflected Gumbel density, restricted to the positive half-line.^[4]
• If X is an exponentially distributed variable with mean 1, then −log(X) has a standard Gumbel distribution.
• If ${\displaystyle X\sim \mathrm {Gumbel} (\alpha _{X},\beta )}$ and ${\displaystyle Y\sim \mathrm {Gumbel} (\alpha _{Y},\beta )}$ are independent, then ${\displaystyle X-Y\sim \mathrm {Logistic} (\alpha _{X}-\alpha _{Y},\beta )\,}$ (see Logistic distribution).
• If ${\displaystyle X,Y\sim \mathrm {Gumbel} (\alpha ,\beta )}$ are independent, then ${\displaystyle X+Y\nsim \mathrm {Logistic} (2\alpha ,\beta )}$. Note that ${\displaystyle E(X+Y)=2\alpha +2\beta \gamma \neq 2\alpha =E\left(\mathrm {Logistic} (2\alpha ,\beta )\right)}$. More generally, the distribution of linear combinations of independent Gumbel random variables can be approximated by GNIG and GIG distributions.^[5]

Theory related to the generalized multivariate log-gamma distribution provides a multivariate version of the Gumbel distribution.

Occurrence and applications

Gumbel has shown that the maximum value (or last order statistic) in a sample of random variables following an exponential distribution minus the natural logarithm of the sample size ^[7] approaches the Gumbel distribution as the sample size increases.^[8]

Concretely, let ${\displaystyle \rho (x)=e^{-x}}$ be the probability distribution of ${\displaystyle x}$ and ${\displaystyle Q(x)=1-e^{-x}}$ its cumulative distribution. Then the maximum value out of ${\displaystyle N}$ realizations of ${\displaystyle x}$ is smaller than ${\displaystyle X}$ if and only if all realizations are smaller than ${\displaystyle X}$. So the cumulative distribution of the maximum value ${\displaystyle {\tilde {x}}}$ satisfies

${\displaystyle P({\tilde {x}}-\log(N)\leq X)=P({\tilde {x}}\leq X+\log(N))=[Q(X+\log(N))]^{N}=\left(1-{\frac {e^{-X}}{N}}\right)^{N},}$

and, for large ${\displaystyle N}$, the right-hand-side converges to ${\displaystyle e^{-e^{(-X)}}.}$

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,^[3] and also to describe droughts.^[9]

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.

In number theory, the Gumbel distribution approximates the number of terms in a random partition of an integer^[10] as well as the trend-adjusted sizes of maximal prime gaps and maximal gaps between prime constellations.^[11]

It appears in the coupon collector's problem.

Gumbel reparametrization tricks

In machine learning, the Gumbel distribution is sometimes employed to generate samples from the categorical distribution. This technique is called "Gumbel-max trick" and is a special example of "reparametrization tricks".^[12]

In detail, let ${\displaystyle (\pi _{1},\ldots ,\pi _{n})}$ be nonnegative, and not all zero, and let ${\displaystyle g_{1},\ldots ,g_{n}}$ be independent samples of Gumbel(0, 1), then by routine integration,

That is, ${\displaystyle \arg \max _{i}(g_{i}+\log \pi _{i})\sim {\text{Categorical}}\left({\frac {\pi _{j}}{\sum _{i}\pi _{i}}}\right)_{j}}$

Equivalently, given any ${\displaystyle x_{1},...,x_{n}\in \mathbb {R} }$, we can sample from its Boltzmann distribution by

Related equations include:^[13]

• If ${\displaystyle x\sim \operatorname {Exp} (\lambda )}$, then ${\displaystyle (-\ln x-\gamma )\sim {\text{Gumbel}}(-\gamma +\ln \lambda ,1)}$.
• ${\displaystyle \arg \max _{i}(g_{i}+\log \pi _{i})\sim {\text{Categorical}}\left({\frac {\pi _{j}}{\sum _{i}\pi _{i}}}\right)_{j}}$.
• ${\displaystyle \max _{i}(g_{i}+\log \pi _{i})\sim {\text{Gumbel}}\left(\log \left(\sum _{i}\pi _{i}\right),1\right)}$. That is, the Gumbel distribution is a max-stable distribution family.
• ${\displaystyle \mathbb {E} [\max _{i}(g_{i}+\beta x_{i})]=\log \left(\sum _{i}e^{\beta x_{i}}\right)+\gamma .}$

Random variate generation

Since the quantile function (inverse cumulative distribution function), ${\displaystyle Q(p)}$, of a Gumbel distribution is given by

${\displaystyle Q(p)=\mu -\beta \ln(-\ln(p)),}$

the variate ${\displaystyle Q(U)}$ has a Gumbel distribution with parameters ${\displaystyle \mu }$ and ${\displaystyle \beta }$ when the random variate ${\displaystyle U}$ is drawn from the uniform distribution on the interval ${\displaystyle (0,1)}$.

Probability paper

In pre-software times probability paper was used to picture the Gumbel distribution (see illustration). The paper is based on linearization of the cumulative distribution function ${\displaystyle F}$ :

${\displaystyle -\ln[-\ln(F)]={\frac {x-\mu }{\beta }}}$

In the paper the horizontal axis is constructed at a double log scale. The vertical axis is linear. By plotting ${\displaystyle F}$ on the horizontal axis of the paper and the ${\displaystyle x}$-variable on the vertical axis, the distribution is represented by a straight line with a slope 1${\displaystyle /\beta }$. When distribution fitting software like CumFreq became available, the task of plotting the distribution was made easier.

See also

• Type-2 Gumbel distribution
• Extreme value theory
• Generalized extreme value distribution
• Fisher–Tippett–Gnedenko theorem
• Emil Julius Gumbel

References

External links

Discrete
univariate

with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric negative Poisson binomial Rademacher soliton discrete uniform Zipf Zipf–Mandelbrot
with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Flory–Schulz Gauss–Kuzmin geometric logarithmic mixed Poisson negative binomial Panjer parabolic fractal Poisson Skellam Yule–Simon zeta

Continuous
univariate

supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular continuous Bernoulli Irwin–Hall Kumaraswamy logit-normal noncentral beta PERT raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle
supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi chi-squared noncentral inverse scaled Dagum Davis Erlang hyper exponential hyperexponential hypoexponential logarithmic F noncentral folded normal Fréchet gamma generalized inverse gamma/Gompertz Gompertz shifted half-logistic half-normal Hotelling's T-squared inverse Gaussian generalized Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal log-t Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami Pareto phase-type Poly-Weibull Rayleigh relativistic Breit–Wigner Rice truncated normal type-2 Gumbel Weibull discrete Wilks's lambda
supported on the whole real line	Cauchy exponential power Fisher's z Kaniadakis κ-Gaussian Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's SU Landau Laplace asymmetric logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t Tracy–Widom variance-gamma Voigt
with support whose type varies	generalized chi-squared generalized extreme value generalized Pareto Marchenko–Pastur Kaniadakis κ-exponential Kaniadakis κ-Gamma Kaniadakis κ-Weibull Kaniadakis κ-Logistic Kaniadakis κ-Erlang q-exponential q-Gaussian q-Weibull shifted log-logistic Tukey lambda

Mixed
univariate

continuous- discrete	Rectified Gaussian

Multivariate
(joint)DirectionalDegenerate
and singularFamilies