copula: evTestK – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

evTestK

Bivariate Test of Extreme-Value Dependence Based on Kendall's Distribution

Description

Test of extreme-value dependence based on the bivariate probability integral transformation. The test statistic is defined in Ben Ghorbal, G. Nešlehová, and Genest (2009).

Usage

evTestK(x, method = c("fsample","asymptotic","jackknife"), ties = NA, N = 100)

Arguments

`x`	a data matrix.
`method`	specifies the variance estimation method; can be either `"fsample"` (finite-sample, the default), `"asymptotic"` or `"jackknife"`.
`ties`	logical; if `TRUE`, the original test is adapted to take the presence of ties in the coordinate samples of `x` into account; the default value of `NA` indicates that the presence/absence of ties will be checked for automatically.
`N`	number of samples to be used to estimate a bias term if `ties = TRUE`.

Details

The code for this test was generously provided by Johanna G. Nešlehová. More details are available in Appendix B of Ben Ghorbal, G. Nešlehová and Genest (2009).

Value

An object of class htest which is a list, some of the components of which are

`statistic`	value of the test statistic.
`p.value`	corresponding p-value.

References

Ghorbal, M. B., Genest, C., and G. Nešlehová, J. (2009) On the test of Ghoudi, Khoudraji, and Rivest for extreme-value dependence. The Canadian Journal of Statistics 37, 1–9.

Kojadinovic, I. (2017). Some copula inference procedures adapted to the presence of ties. Computational Statistics and Data Analysis 112, 24–41, https://arxiv.org/abs/1609.05519.

Examples

set.seed(321)
## Do the data come from an extreme-value copula?
evTestK(Ug <- rCopula(200, gumbelCopula(3))) # not significant => yes, EV
    dim(Uc <- rCopula(200, claytonCopula(3)))
## Clayton:                       tests are highly significant => no, not EV
(K1 <- evTestK(Uc))
(K2 <- evTestK(Uc, method = "asymptotic"))

system.time(print(K3 <- evTestK(Uc, method = "jackknife")))
## slower: 1.06 sec (2015 intel core i7)

copula

Multivariate Dependence with Copulas

v1.0-1

GPL (>= 3) | file LICENCE

Authors

Marius Hofert [aut] (<https://orcid.org/0000-0001-8009-4665>), Ivan Kojadinovic [aut] (<https://orcid.org/0000-0002-2903-1543>), Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Jun Yan [aut] (<https://orcid.org/0000-0003-4401-7296>), Johanna G. Nešlehová [ctb] (evTestK(), <https://orcid.org/0000-0001-9634-4796>), Rebecca Morger [ctb] (fitCopula.ml(): code for free mixCopula weight parameters)

Initial release

2020-12-07