dot-rmevA1: Multivariate extreme value distribution sampling algorithm...
In mev: Modelling of Extreme Values

.rmevA1

R Documentation

Multivariate extreme value distribution sampling algorithm via angular measure

Description

This algorithm corresponds to Algorithm 1 in Dombry, Engelke and Oesting (2016), using the formulation of the Dirichlet mixture of Coles and Tawn (1991) as described and derived in Boldi (2009) for the bilogistic and extremal Dirichlet model. Models currently implemented include logistic, negative logistic, extremal Dirichlet and bilogistic MEV.

Usage

.rmevA1(n, d, para, model, Sigma, loc)

Arguments

`n`	sample size
`d`	dimension of the multivariate distribution
`model`	integer, currently ranging from 1 to 9, corresponding respectively to (1) `log`, (2) `neglog`, (3) `dirmix`, (4) `bilog`, (5) `extstud`, (6) `br`, (7) `ct` and `sdir`, (8) `smith` and (9) `hr`.
`Sigma`	covariance matrix for Brown-Resnick, Smith and extremal student. Conditionally negative definite matrix of parameters for the Huesler–Reiss model. Default matrix for compatibility
`loc`	matrix of location for Smith model.
`param`	a vector of parameters