evTestA  R Documentation 
Test of bivariate extremevalue dependence based on the process comparing the empirical copula with a natural nonparametric estimator of the unknown copula derived under extremevalue dependence. The test statistics are defined in the third reference. Approximate pvalues 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 pseudoobservations. 
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 lowlevel 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 pvalue. 
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 pvalue.
Genest, C. and Segers, J. (2009). Rankbased inference for bivariate extremevalue copulas. Annals of Statistics, 37, pages 29903022.
Rémillard, B. and Scaillet, O. (2009). Testing for equality between two copulas. Journal of Multivariate Analysis, 100(3), pages 377386.
Kojadinovic, I. and Yan, J. (2010). Nonparametric rankbased tests of bivariate extremevalue dependence. Journal of Multivariate Analysis 101, 2234–2249.
evTestK
, evTestC
,
evCopula
,
gofEVCopula
, An
.
## Do these data come from an extremevalue copula? set.seed(63) uG < rCopula(100, gumbelCopula (3)) uC < rCopula(100, claytonCopula(3)) ## these two take 21 sec on nbmm4 (Intel Core i75600U @ 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 pvalue even though Clayton is *NOT* an EV copula.
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