GEV: Create a Generalised Extreme Value (GEV) distribution

In distributions3: Probability Distributions as S3 Objects

Create a Generalised Extreme Value (GEV) distribution

Description

The GEV distribution arises from the Extremal Types Theorem, which is rather like the Central Limit Theorem (see ⁠\link{Normal}⁠) but it relates to the maximum of n i.i.d. random variables rather than to the sum. If, after a suitable linear rescaling, the distribution of this maximum tends to a non-degenerate limit as n tends to infinity then this limit must be a GEV distribution. The requirement that the variables are independent can be relaxed substantially. Therefore, the GEV distribution is often used to model the maximum of a large number of random variables.

Usage

GEV(mu = 0, sigma = 1, xi = 0)

Arguments

mu	The location parameter, written \mu in textbooks. mu can be any real number. Defaults to 0.
sigma	The scale parameter, written \sigma in textbooks. sigma can be any positive number. Defaults to 1.
xi	The shape parameter, written \xi in textbooks. xi can be any real number. Defaults to 0, which corresponds to a Gumbel 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 GEV random variable with location parameter mu = \mu, scale parameter sigma = \sigma and shape parameter xi = \xi.

Support: (-\infty, \mu - \sigma / \xi) for \xi < 0; (\mu - \sigma / \xi, \infty) for \xi > 0; and R, the set of all real numbers, for \xi = 0.

Mean: \mu + \sigma[\Gamma(1 - \xi) - 1]/\xi for \xi < 1, \xi \neq 0; \mu + \sigma\gamma for \xi = 0, where \gamma is Euler's constant, approximately equal to 0.57722; undefined otherwise.

Median: \mu + \sigma[(\ln 2) ^ {-\xi} - 1]/\xi for \xi \neq 0; \mu - \sigma\ln(\ln 2) for \xi = 0.

Variance: \sigma^2 [\Gamma(1 - 2 \xi) - \Gamma(1 - \xi)^2] / \xi^2 for \xi < 1 / 2, \xi \neq 0; \sigma^2 \pi^2 / 6 for \xi = 0; undefined otherwise.

Probability density function (p.d.f):

If \xi \neq 0 then

f(x) = \sigma ^ {-1} [1 + \xi (x - \mu) / \sigma] ^ {-(1 + 1/\xi)}% \exp\{-[1 + \xi (x - \mu) / \sigma] ^ {-1/\xi} \}

for 1 + \xi (x - \mu) / \sigma > 0. The p.d.f. is 0 outside the support.

In the \xi = 0 (Gumbel) special case

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):

If \xi \neq 0 then

F(x) = \exp\{-[1 + \xi (x - \mu) / \sigma] ^ {-1/\xi} \}

for 1 + \xi (x - \mu) / \sigma > 0. The c.d.f. is 0 below the support and 1 above the support.

In the \xi = 0 (Gumbel) special case

F(x) = \exp\{-\exp[-(x - \mu) / \sigma] \}

for x in R, the set of all real numbers.

Value

A GEV object.

See Also

Other continuous distributions: Beta(), Cauchy(), ChiSquare(), Erlang(), Exponential(), FisherF(), Frechet(), GP(), Gamma(), Gumbel(), LogNormal(), Logistic(), Normal(), RevWeibull(), StudentsT(), Tukey(), Uniform(), Weibull()

Examples

set.seed(27)

X <- GEV(1, 2, 0.1)
X

random(X, 10)

pdf(X, 0.7)
log_pdf(X, 0.7)

cdf(X, 0.7)
quantile(X, 0.7)

cdf(X, quantile(X, 0.7))
quantile(X, cdf(X, 0.7))

distributions3 documentation built on Sept. 30, 2024, 9:37 a.m.