copula (version 0.999-15)

evTestK: Bivariate Test of Extreme-Value Dependence Based on Kendall's Process

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"))

Arguments

x

a data matrix.

method

specifies the variance estimation method; can be either "fsample" (finite-sample, the default), "asymptotic" or "jackknife".

Value

Returns a list whose attributes are:

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).

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.

See Also

evTestC, evTestA, evCopula, gofEVCopula, An.

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)