gofEVCopula {copula} | R Documentation |
Goodness-of-fit tests for extreme-value copulas based on the empirical process comparing one of the two nonparameteric rank-based estimator of the Pickands dependence function studied in Genest and Segers (2009) with a parametric estimate of the Pickands dependence function derived under the null hypothesis. The test statistic is the Cramer-von Mises functional Sn defined in Equation (5) of Genest, Kojadinovic, G. Nešlehová, and Yan (2010). Approximate p-values for the test statistic are obtained using a parametric bootstrap.
gofEVCopula(copula, x, N = 1000, method = "mpl", estimator = "CFG", m = 1000, verbose = TRUE, print.every = NULL, optim.method = "Nelder-Mead")
copula |
object of class |
x |
a data matrix that will be transformed to pseudo-observations. |
N |
number of bootstrap samples to be used to simulate realizations of the test statistic under the null hypothesis. |
method |
estimation method to be used to estimate the
dependence parameter(s); can be either |
estimator |
specifies which nonparametric rank-based estimator
of the unknown Pickands dependence function to use; can be either
|
m |
number of points of the uniform grid on [0,1] used to compute the test statistic numerically. |
print.every |
is deprecated in favor of |
verbose |
a logical specifying if progress of the bootstrap
should be displayed via |
optim.method |
the method for |
More details can be found in the second reference.
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 p-value. |
parameter |
estimates of the parameters for the hypothesized copula family. |
For a given degree of dependence, the most popular extreme-value copulas are strikingly similar.
Genest, C. and Segers, J. (2009). Rank-based inference for bivariate extreme-value copulas. Annals of Statistics 37, 2990–3022.
Genest, C. Kojadinovic, I., G. Nešlehová, J., and Yan, J. (2011). A goodness-of-fit test for bivariate extreme-value copulas. Bernoulli 17(1), 253–275.
evCopula
, evTestC
, evTestA
,
evTestK
, gofCopula
, An
.
## Not run: x <- rCopula(100, claytonCopula(3)) ## Does the Gumbel family seem to be a good choice? gofEVCopula(gumbelCopula(1), x) ## The same with different estimation methods gofEVCopula(gumbelCopula(1), x, method="itau") gofEVCopula(gumbelCopula(1), x, method="irho") ## The same with different extreme-value copulas gofEVCopula(galambosCopula(1), x) gofEVCopula(galambosCopula(1), x, method="itau") gofEVCopula(galambosCopula(1), x, method="irho") gofEVCopula(huslerReissCopula(1), x) gofEVCopula(huslerReissCopula(1), x, method="itau") gofEVCopula(huslerReissCopula(1), x, method="irho") gofEVCopula(tevCopula(0, df.fixed=TRUE), x) gofEVCopula(tevCopula(0, df.fixed=TRUE), x, method="itau") gofEVCopula(tevCopula(0, df.fixed=TRUE), x, method="irho") ## End(Not run)