evTestA | R Documentation |
Test of bivariate extreme-value dependence based on the process comparing the empirical copula with a natural nonparametric estimator of the unknown copula derived under extreme-value dependence. The test statistics are defined in the third reference. Approximate p-values for the test statistics are obtained by means of a multiplier technique.
evTestA(x, N = 1000, derivatives = c("An","Cn"),
ties.method = eval(formals(rank)$ties.method),
trace.lev = 0, report.err = FALSE)
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. |
derivatives |
string specifying how the derivatives of the unknown
copula are estimated, either |
ties.method |
|
trace.lev |
integer indicating the level of diagnostic tracing to be printed to the console (from low-level algorithm). |
report.err |
|
More details are available in the third reference. See also Genest and Segers (2009) and Remillard and Scaillet (2009).
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. |
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.
Genest, C. and Segers, J. (2009). Rank-based inference for bivariate extreme-value copulas. Annals of Statistics, 37, pages 2990-3022.
Rémillard, B. and Scaillet, O. (2009). Testing for equality between two copulas. Journal of Multivariate Analysis, 100(3), pages 377-386.
Kojadinovic, I. and Yan, J. (2010). Nonparametric rank-based tests of bivariate extreme-value dependence. Journal of Multivariate Analysis 101, 2234–2249.
evTestK
, evTestC
,
evCopula
,
gofEVCopula
, An
.
## Do these data come from an extreme-value copula?
set.seed(63)
uG <- rCopula(100, gumbelCopula (3))
uC <- rCopula(100, claytonCopula(3))
## these two take 21 sec on nb-mm4 (Intel Core i7-5600U @ 2.60GHz):
evTestA(uG)
evTestA(uC) # significant even though Clayton is *NOT* an extreme value copula
## These are fast:
evTestA(uG, derivatives = "Cn")
evTestA(uC, derivatives = "Cn") # small p-value even though Clayton is *NOT* an EV copula.
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