R: Large-sample Test of Multivariate Extreme-Value Dependence
evTestC
R Documentation
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.
Details
More details are available in the second reference.
See also Remillard and Scaillet (2009).
Value
Returns a list whose attributes are:
statistic
value of the test statistic.
p.value
corresponding approximate p-value.
Note
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. One way of dealing with ties was suggested in the
last reference.
References
R<c3><83><c2><a9>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 Statistics39, 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)))