where N is the number of observations (length of u1) and
hat{τ} the empirical Kendall's tau of the data vectors u1
and u2. The p-value of the null hypothesis of bivariate independence
hence is asymptotically
p.value = 2*(1-Φ(T)),
where Φ is the standard normal distribution function.
Value
statistic
Test statistic of the independence test.
p.value
P-value of the independence test.
Author(s)
Jeffrey Dissmann
References
Genest, C. and A. C. Favre (2007). Everything you always wanted
to know about copula modeling but were afraid to ask. Journal of Hydrologic
Engineering, 12 (4), 347-368.
## Example 1: Gaussian copula with large dependence parameter
cop <- BiCop(1, 0.7)
dat <- BiCopSim(500, cop)
# perform the asymptotic independence test
BiCopIndTest(dat[, 1], dat[, 2])
## Example 2: Gaussian copula with small dependence parameter
cop <- BiCop(1, 0.01)
dat <- BiCopSim(500, cop)
# perform the asymptotic independence test
BiCopIndTest(dat[, 1], dat[, 2])