Gumbel distribution

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

In probability theory and statistics, the Gumbel distribution (Generalized Extreme Value distribution Type-I) 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 rest of this article refers to the Gumbel 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]

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 $\gamma \approx 0.5772$ is the Euler–Mascheroni constant. 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 = (n-1)! \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 half-line.^[3]
If X is an exponential with mean 1, then -log(X) has a standard Gumbel-Distribution.

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

Probability paper

A piece of graph paper that incorporates the Gumbel distribution.

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 $F$ :

-\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 $x$ -variable 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.

Notice for Wolfram MathWorld and MATLAB users

The Gumbel Distribution in Wolfram MathWorld and MATLAB^[4] is used to refer to the distribution corresponding to a minimum extreme value distribution which is not same with the Gumbel distribution in Wikipedia (which models the maximum); in this case to model the maximum value, use the negative of the original values, that is, the negative of $x$ and the negative of the mode $\mu$ .

Application

Distribution fitting with confidence band of a cumulative Gumbel distribution to maximum one-day October rainfalls.

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.^[5]

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,^[6] and also to describe droughts.^[7]

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 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^[8] as well as the trend-adjusted sizes of record prime gaps and record gaps between prime constellations.^[9]

In machine learning, the Gumbel distribution is sometimes employed to generate samples from the categorical distribution for example.^[10]

References

↑ 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.
↑ 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.
↑ MATLAB documentation: Extreme Value Distribution (HTML) .
↑ 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. ASIN B0007DSHG4.
↑ 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. ISBN 90-70754-33-9.
↑ 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.
↑ 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. doi:10.1215/S0012-7094-41-00826-8.
↑ Kourbatov, A. (2013). "Maximal gaps between prime k-tuples: a statistical approach". Journal of Integer Sequences. 16. arXiv:1301.2242. Article 13.5.2.
↑ Adams, Ryan. "The Gumbel-Max Trick for Discrete Distributions".

External links

Wikimedia Commons has media related to:

Gumbel distribution (category)

Probability distributions

List

Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot

Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta

Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle

Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda

Continuous univariate supported on the whole real line	Cauchy exponential power Fisher's z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric Laplace logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt

Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Tukey lambda q-Gaussian q-exponential q-Weibull shifted log-logistic

Mixed continuous-discrete univariate	rectified Gaussian

Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart

Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham

Degenerate and singular	Degenerate Dirac delta function Singular Cantor

Families	Circular compound Poisson elliptical exponential natural exponential location-scale maximum entropy mixture Pearson Tweedie wrapped

This article is issued from Wikipedia - version of the 10/26/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.