copula (version 0.999-13)

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.
• p.value
  corresponding approximate p-value.

Details

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

References

Ré{e}millard, 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.

Kojadinovic, I. and Yan, J. (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, 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)))
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))