emp_vario: Estimation of the variogram matrix \eGamma of a Huesler-Reiss...

In sebastian-engelke/graphicalExtremes: Statistical Methodology for Graphical Extreme Value Models

Estimation of the variogram matrix \eGamma of a Huesler-Reiss distribution

Description

Estimates the variogram of the Huesler-Reiss distribution empirically.

Usage

emp_vario(data, k = NULL, p = NULL)

emp_vario_pairwise(data, k = NULL, p = NULL, verbose = FALSE)

Arguments

data	Numeric \nxd matrix, where n is the number of observations and d is the dimension.
k	Integer between 1 and d. Component of the multivariate observations that is conditioned to be larger than the threshold p. If NULL (default), then an average over all k is returned.
p	Numeric between 0 and 1 or NULL. If NULL (default), it is assumed that the data are already on multivariate Pareto scale. Else, p is used as the probability in the function data2mpareto() to standardize the data.
verbose	Print verbose progress information

Details

emp_vario_pairwise calls emp_vario for each pair of observations. This is more robust if the data contains many NAs, but can take rather long.

Value

Numeric \dxd matrix. The estimated variogram of the Huesler-Reiss distribution.

See Also

Other parameter estimation methods: data2mpareto(), emp_chi(), emp_chi_multdim(), emtp2(), fmpareto_HR_MLE(), fmpareto_graph_HR(), loglik_HR()

Examples

G <- generate_random_Gamma(d=5)
y <- rmpareto(n=100, par=G)
Ghat <- emp_vario(y)

sebastian-engelke/graphicalExtremes documentation built on Jan. 10, 2025, 10:02 a.m.