copula (version 1.1-4)

evTestC: Large-sample Test of Multivariate Extreme-Value Dependence

Description

Test of multivariate extreme-value dependence based on the empirical copula and max-stability. The test statistics are defined in the second reference. Approximate p-values for the test statistics are obtained by means of a multiplier technique.

Usage

evTestC(x, N = 1000)

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 approximate p-value.

Arguments

x

a data matrix that will be transformed to pseudo-observations.

N

number of multiplier iterations to be used to simulate realizations of the test statistic under the null hypothesis.

Details

More details are available in the second reference. See also Remillard and Scaillet (2009).

References

Rémillard, B. and Scaillet, O. (2009). Testing for equality between two copulas. Journal of Multivariate Analysis, 100(3), pages 377-386.

Kojadinovic, I., Segers, J., and Yan, J. (2011). Large-sample tests of extreme-value dependence for multivariate copulas. The Canadian Journal of Statistics 39, 4, pages 703-720.

See Also

evTestK, evTestA, evCopula, gofEVCopula, An.

Examples

## Do these data come from an extreme-value copula?
evTestC(rCopula(200, gumbelCopula(3)))
evTestC(rCopula(200, claytonCopula(3)))

## Three-dimensional examples
evTestC(rCopula(200, gumbelCopula(3, dim=3)))
evTestC(rCopula(200, claytonCopula(3, dim=3)))
# \dontshow{
 set.seed(101)
 G.t <- evTestC(rCopula(200, gumbelCopula(3, dim=3)))
 C.t <- evTestC(rCopula(200, claytonCopula(3, dim=3)))
 eT3 <- evTestC(rCopula(200, tevCopula(.8, df=3)))
 stopifnot(all.equal(G.t$p.value, 0.1543,       tolerance=.001),
           all.equal(C.t$p.value, 4995/9999990, tolerance= 1e-7),
           all.equal(eT3$p.value, 0.407092907092907, tolerance= 1e-7))
# }