copula (version 0.99-4)

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)

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.

Value

• Returns a list whose attributes are:
• statistic
  value of the test statistic.
• pvalue
  corresponding approximate p-value.

Details

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

References

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

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

I. Kojadinovic and J. Yan (2010). Modeling Multivariate Distributions with Continuous Margins Using the copula R Package. Journal of Statistical Software, 34(9), pages 1-20.

See Also

evTestK, evTestA, evCopula, gofEVCopula, Anfun.

Examples

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

## Three-dimensional examples
evTestC(rcopula(gumbelCopula(3, dim=3), 200))
evTestC(rcopula(claytonCopula(3, dim=3), 200))