mev (version 1.17)

expme: Exponent measure for multivariate generalized Pareto distributions

Description

Integrated intensity over the region defined by \([0, z]^c\) for logistic, Huesler-Reiss, Brown-Resnick and extremal Student processes.

Usage

expme(
  z,
  par,
  model = c("log", "neglog", "hr", "br", "xstud"),
  method = c("TruncatedNormal", "mvtnorm", "mvPot")
)

Value

numeric giving the measure of the complement of \([0,z]\).

Arguments

z

vector at which to estimate exponent measure

par

list of parameters

model

string indicating the model family

method

string indicating the package from which to extract the numerical integration routine

Examples

if (FALSE) {
# Extremal Student
Sigma <- stats::rWishart(n = 1, df = 20, Sigma = diag(10))[, , 1]
expme(z = rep(1, ncol(Sigma)), par = list(Sigma = cov2cor(Sigma), df = 3), model = "xstud")
# Brown-Resnick model
D <- 5L
loc <- cbind(runif(D), runif(D))
di <- as.matrix(dist(rbind(c(0, ncol(loc)), loc)))
semivario <- function(d, alpha = 1.5, lambda = 1) {
  (d / lambda)^alpha
}
Vmat <- semivario(di)
Lambda <- Vmat[-1, -1] / 2
expme(z = rep(1, ncol(Lambda)), par = list(Lambda = Lambda), model = "br", method = "mvPot")
Sigma <- outer(Vmat[-1, 1], Vmat[1, -1], "+") - Vmat[-1, -1]
expme(z = rep(1, ncol(Lambda)), par = list(Lambda = Lambda), model = "br", method = "mvPot")
}