emp_chi {graphicalExtremes}

Empirical estimation of extremal correlation matrix \chi

Description

Estimates empirically the matrix of bivariate extremal correlation coefficients \chi.

Usage

emp_chi(data, p = NULL)

emp_chi_pairwise(data, p = NULL, verbose = FALSE)

Arguments

data

Numeric n \times d matrix, where n is the number of observations and d is the dimension.

p

Numeric scalar 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 data2mpareto() to standardize the data.

verbose

Print verbose progress information

Details

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

Value

Numeric matrix d \times d. The matrix contains the bivariate extremal coefficients \chi_{ij}, for i, j = 1, ..., d.

See Also

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

Examples

n <- 100
d <- 4
p <- .8
Gamma <- cbind(
  c(0, 1.5, 1.5, 2),
  c(1.5, 0, 2, 1.5),
  c(1.5, 2, 0, 1.5),
  c(2, 1.5, 1.5, 0)
)

set.seed(123)
my_data <- rmstable(n, "HR", d = d, par = Gamma)
emp_chi(my_data, p)