Independence Test for Bivariate Copula Data
This function returns the p-value of a bivariate asymptotic independence test based on Kendall's τ.
BiCopIndTest(u1, u2)
u1, u2 |
Data vectors of equal length with values in [0,1]. |
The test exploits the asymptotic normality of the test statistic
statistic := T = ( (9N(N-1)) / (2(2N+5)) )^0.5 * |τ|,
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.
statistic |
Test statistic of the independence test. |
p.value |
P-value of the independence test. |
Jeffrey Dissmann
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])
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.