The Gumbel distribution is a special case of the \link{GEV}
distribution,
obtained when the GEV shape parameter \(\xi\) is equal to 0.
It may be referred to as a type I extreme value distribution.
Details
We recommend reading this documentation on https://alexpghayes.github.io/distributions3/, where the math will render with additional detail and much greater clarity.
In the following, let \(X\) be a Gumbel random variable with location
parameter mu
= \(\mu\), scale parameter sigma
= \(\sigma\).
Support: \(R\), the set of all real numbers.
Mean: \(\mu + \sigma\gamma\), where \(\gamma\) is Euler's constant, approximately equal to 0.57722.
Median: \(\mu - \sigma\ln(\ln 2)\).
Variance: \(\sigma^2 \pi^2 / 6\).
Probability density function (p.d.f):
$$f(x) = \sigma ^ {-1} \exp[-(x - \mu) / \sigma]% \exp\{-\exp[-(x - \mu) / \sigma] \}$$ for \(x\) in \(R\), the set of all real numbers.
Cumulative distribution function (c.d.f):
In the \(\xi = 0\) (Gumbel) special case $$F(x) = \exp\{-\exp[-(x - \mu) / \sigma] \}$$ for \(x\) in \(R\), the set of all real numbers.
Examples
set.seed(27)
X <- Gumbel(1, 2)
X
#> [1] "Gumbel(mu = 1, sigma = 2)"
random(X, 10)
#> [1] 8.104751940 -0.816379582 5.007573903 0.789488808 0.183959497
#> [6] 1.183838833 -0.929543900 -2.587372533 -0.373340977 -0.002439646
pdf(X, 0.7)
#> [1] 0.1817758
log_pdf(X, 0.7)
#> [1] -1.704981
cdf(X, 0.7)
#> [1] 0.3129117
quantile(X, 0.7)
#> [1] 3.061861
cdf(X, quantile(X, 0.7))
#> [1] 0.7
quantile(X, cdf(X, 0.7))
#> [1] 0.7