data2mpareto: Data standardization to multivariate Pareto scale

In graphicalExtremes: Statistical Methodology for Graphical Extreme Value Models

Data standardization to multivariate Pareto scale

Description

Transforms the data matrix empirically to the multivariate Pareto scale.

Usage

data2mpareto(data, p, na.rm = FALSE)

Arguments

data	Numeric \nxd matrix, where n is the number of observations and d is the dimension.
p	Numeric between 0 and 1. Probability used for the quantile to threshold the data.
na.rm	Logical. If rows containing NAs should be removed.

Details

The columns of the data matrix are first transformed empirically to standard Pareto distributions. Then, only the observations where at least one component exceeds the p-quantile of the standard Pareto distribution are kept. Those observations are finally divided by the p-quantile of the standard Pareto distribution to standardize them to the multivariate Pareto scale.

If na.rm is FALSE, missing entries are left as such during the transformation of univariate marginals. In the thresholding step, missing values are considered as -Inf.

Value

Numeric \eXTimesYmd matrix, where m is the number of rows in the original data matrix that are above the threshold.

See Also

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

Other structure estimation methods: eglearn(), emst(), fit_graph_to_Theta()

Examples

n <- 20
d <- 4
p <- .8
G <- 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 = G)
data2mpareto(my_data, p)

graphicalExtremes documentation built on Nov. 14, 2023, 1:07 a.m.