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Gumbel

Probability density function
Cumulative distribution function
Parameters	${\displaystyle \mu \!}$ location (real) ${\displaystyle \beta >0\!}$ scale (real)
Support	${\displaystyle x\in (-\infty ;+\infty )\!}$
PDF	${\displaystyle {\frac {1}{\beta }}e^{-(z+e^{-z})}\!}$ where ${\displaystyle z={\frac {x-\mu }{\beta }}\!}$
CDF	${\displaystyle e^{-e^{-(x-\mu )/\beta }}\!}$
Mean	${\displaystyle \mu +\beta \,\gamma \!}$
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 is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions. Such a 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 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 often incorrectly labelled as Gompertz distribution.

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).

Properties

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The cumulative distribution function of the Gumbel distribution is

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

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 ${\displaystyle \gamma }$ = Euler–Mascheroni constant ${\displaystyle \approx 0.5772.}$ The standard deviation is ${\displaystyle \beta \pi /{\sqrt {6}}.}$

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 ${\displaystyle \gamma }$, and the standard deviation is ${\displaystyle \pi /{\sqrt {6}}\approx 1.2825.}$

Quantile function and Generating Gumbel variates

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 ${\displaystyle (0,1)}$.

Related distributions

When the cdf of Y is the converse of the Gumbel standard cumulative distribution, ${\displaystyle P(Y\leq y)=1-F(y)}$, then Y has a density function that is a Gompertz function: however, Y does not have a Gompertz distribution since the Gompertz distribution is restricted to positive values while Y can take both positive and negative values.

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

Graphic paper

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

${\displaystyle -ln=(x-\mu )/\beta }$

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, 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.

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, and also to describe droughts.

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 one-day October rainfalls showing also the 90% confidence belt 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 as well as the trend-adjusted sizes of record prime gaps and record gaps between prime constellations.

See also

• Type-1 Gumbel distribution
• Type-2 Gumbel distribution
• Extreme value theory
• Generalized extreme value distribution
• Fisher–Tippett–Gnedenko theorem

External links

References

1. Willemse, W.J.; Kaas, R. (2007). "Rational reconstruction of frailty-based mortality models by a generalisation of Gompertz' law of mortality". Insurance: Mathematics and Economics. 40 (3): 468. doi:10.1016/j.insmatheco.2006.07.003.
2. Gumbel, E. J. (1954). Statistical theory of extreme values and some practical applications. Applied Mathematics Series. Vol. 33 (1st ed.). U.S. Department of Commerce, National Bureau of Standards. ASIN B0007DSHG4. LCCN 1 .U58 no.33 QA 1 .U58 no.33. {{cite book}}: Check |lccn= value (help)
3. Oosterbaan, R. J. (1994). "Chapter 6 Frequency and Regression Analysis". In Ritzema, first=H. P. (ed.). Drainage Principles and Applications, Publication 16 (PDF). Wageningen, The Netherlands: International Institute for Land Reclamation and Improvement (ILRI). pp. 175–224. ISBN 90-70754-33-9. {{cite book}}: Missing pipe in: |editor-first= (help)
4. 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. doi:10.1016/j.jhydrol.2010.04.035.
5. Erdös, Paul; Lehner, Joseph (1941). "Of a positive integer". Duke Mathematical Journal. 8 (2): 335. doi:10.1215/S0012-7094-41-00826-8.
6. Kourbatov, A. (2013). "Maximal gaps between prime k-tuples: a statistical approach". Journal of Integer Sequences. 16. arXiv:1301.2242. Article 13.5.2.

• Continuous distributions
• Extreme value data