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

View source: R/evTests.R

evTestCR Documentation

Large-sample Test of Multivariate Extreme-Value Dependence


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.


evTestC(x, N = 1000)



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


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


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


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


value of the test statistic.


corresponding approximate p-value.


This test was derived under the assumption of continuous margins, which implies that ties occur with probability zero. The presence of ties in the data might substantially affect the approximate p-value.


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.


## 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)))

copula documentation built on June 15, 2022, 5:07 p.m.