evTestK {copula} | R Documentation |
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
|
ties |
logical; if |
N |
number of samples to be used to estimate a bias term if
|
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.
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)