Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

GIG

Generalized Inverse Gaussian Distribution

Description

Calculates (log) moments of univariate generalized inverse Gaussian (GIG) distribution and generating random variates.

Usage

EGIG(lambda, chi, psi, k = 1)
ElogGIG(lambda, chi, psi)
rGIG(n, lambda, chi, psi, envplot = FALSE, messages = FALSE)

Arguments

chi

numeric, chi parameter.
envplot

logical, whether plot of rejection envelope should be created.
k

integer, order of moments.
lambda

numeric, lambda parameter.
messages

logical, whether a message about rejection rate should be returned.
n

integer, count of random variates.
psi

numeric, psi parameter.

Details

Normal variance mixtures are frequently obtained by perturbing the variance component of a normal distribution; here this is done by multiplying the square root of a mixing variable assumed to have a GIG distribution depending upon three parameters (lambda, chi, psi). See p.77 in QRM.
Normal mean-variance mixtures are created from normal variance mixtures by applying another perturbation of the same mixing variable to the mean component of a normal distribution. These perturbations create Generalized Hyperbolic Distributions. See pp. 78–81 in QRM. A description of the GIG is given on page 497 in QRM Book.

Value

(log) mean of distribution or vector random variates in case of rgig().

QRM

Provides R-Language Code to Examine Quantitative Risk Management Concepts

v0.4-31
GPL (>= 2)
Authors
Bernhard Pfaff [aut, cre], Marius Hofert [ctb], Alexander McNeil [aut] (S-Plus original (QRMlib)), Scott Ulmann [trl] (First R port as package QRMlib)
Initial release
2020-02-15

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.