FGM: The Eyraud Farlie Gumbel Morgenstern Distribution

In RTDE: Robust Tail Dependence Estimation

The Eyraud Farlie Gumbel Morgenstern Distribution

Description

Density function, distribution function, quantile function, random generation.

Usage

dFGM(u, v, alpha, log = FALSE)
pFGM(u, v, alpha, lower.tail=TRUE, log.p = FALSE)
qFGM(p, alpha, lower.tail=TRUE, log.p = FALSE)
rFGM(n, alpha)

Arguments

u, v	vector of quantiles.
p	vector of probabilities.
n	number of observations. If length(n) > 1, the length is taken to be the number required.
alpha	shape parameter.
log, log.p	logical; if TRUE, probabilities p are given as log(p).
lower.tail	logical; if TRUE (default), probabilities are P[X \le x], otherwise, P[X > x].

Details

The FGM is defined by the following distribution function

C(u,v) = u*v*(1+\alpha*(1-u)*(1-v))

for all u,v in [0,1] and \alpha in [0,1]. When lower.tail=FALSE, pFGM returns the survival copula P(U > u, V > v).

Value

dFGM gives the density, pFGM gives the distribution function, qFGM gives the quantile function, and rFGM generates random deviates.

The length of the result is determined by n for rFGM, and is the maximum of the lengths of the numerical parameters for the other functions.

The numerical parameters other than n are recycled to the length of the result. Only the first elements of the logical parameters are used.

Author(s)

Christophe Dutang

References

Nelsen, R. (2006), An Introduction to Copula, Second Edition, Springer.

Examples

#####
# (1) density function
u <- v <- seq(0, 1, length=25)

cbind(u, v, dFGM(u, v, 1/2))
cbind(u, v, outer(u, v, dFGM, alpha=1/2))


#####
# (2) distribution function

cbind(u, v, pFGM(u, v, 1/2))
cbind(u, v, outer(u, v, pFGM, alpha=1/2))




		

RTDE documentation built on Oct. 16, 2024, 5:08 p.m.