BiCopIndTest {CDVine} | R Documentation |
This function returns the p-value of a bivariate asymptotic independence test based on Kendall's tau.
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.
BiCopPar2Tau
, BiCopTau2Par
, BiCopSelect
, CDVineCopSelect
## Example 1: Gaussian copula with large dependence parameter par1 = 0.7 fam1 = 1 dat1 = BiCopSim(500,fam1,par1) # perform the asymptotic independence test BiCopIndTest(dat1[,1],dat1[,2]) ## Example 2: Gaussian copula with small dependence parameter par2 = 0.01 fam2 = 1 dat2 = BiCopSim(500,fam2,par2) # perform the asymptotic independence test BiCopIndTest(dat2[,1],dat2[,2])