tailind.test {POT} | R Documentation |
Testing for Tail Independence in Extreme Value Models
Description
Several tests for tail independence (e.g. asymptotic independence) for a bivariate extreme value distribution
Usage
tailind.test(data, c = -0.1, emp.trans = TRUE, chisq.n.class = 4)
Arguments
data |
A matrix with two columns given the data. |
c |
A negative numeric. Must be close to zero to approximate accurately asymptotic results. |
emp.trans |
Logical. If |
chisq.n.class |
A numeric given the number of classes for the Chi squared test. |
Details
These tests are based on an asymptotic results shown by Falk and Michel
(2006). Let be a random vector which follows in its
upper tail a bivariate extreme value distribution with reverse
exponential margins. The conditional distribution function of
, given that
, converges to
,
, if
iff
and
are
asymptotically independent. Otherwise, the limit is
Value
This function returns a table with the Neymann-Pearson, Fisher, Kolmogorov-Smirnov and Chi-Square statistics and the related p-values.
Author(s)
Mathieu Ribatet
References
Falk, M. and Michel, Rene(2006) Testing for tail independence in extreme value models. Annals of the Institute of Statistical Mathematics 58: 261–290
See Also
Examples
##A total independence example
x <- rbvgpd(7000, alpha = 1, mar1 = c(0, 1, 0.25))
tailind.test(x)
##An asymptotically dependent example
y <- rbvgpd(7000, alpha = 0.75, model = "nlog", mar1 = c(0, 1, 0.25),
mar2 = c(2, 0.5, -0.15))
tailind.test(y)
##A perfect dependence example
z <- rnorm(7000)
tailind.test(cbind(z, 2*z - 5))