gofPIOSTn
(Package: gofCopula) :
2 and 3 dimensional gof test based on the in-and-out-of-sample approach
gofPIOSTn tests a 2 or 3 dimensional dataset with the PIOS test for a copula. The possible copulae are "normal", "t", "gumbel", "clayton" and "frank". The parameter estimation is performed with pseudo maximum likelihood method. In case the estimation fails, inversion of Kendall's tau is used. The approximate p-values are computed with a semiparametric bootstrap, which computation can be accelerated by enabling in-build parallel computation.
gofRosenblattSnC contains the SnC gof test from Genest (2009) for copulae and compares the empirical copula against a parametric estimate of the copula derived under the null hypothesis. The margins can be estimated by a bunch of distributions and the time which is necessary for the estimation can be given. The approximate p-values are computed with a parametric bootstrap, which computation can be accelerated by enabling in-build parallel computation. The gof statistics are computed with the function gofTstat from the package copula. It is possible to insert datasets of all dimensions above 1 and the possible copulae are "normal", "t", "gumbel", "clayton" and "frank". The parameter estimation is performed with pseudo maximum likelihood method. In case the estimation fails, inversion of Kendall's tau is used.
gofKernel
(Package: gofCopula) :
2 and 3 dimensional gof test of Scaillet
gofKernel tests a 2 or 3 dimensional dataset with the Scaillet test for a copula. The possible copulae are "normal", "t", "gumbel", "clayton" and "frank". The parameter estimation is performed with pseudo maximum likelihood method. In case the estimation fails, inversion of Kendall's tau is used. The approximate p-values are computed with a parametric bootstrap, which computation can be accelerated by enabling in-build parallel computation.
gof
(Package: gofCopula) :
Combining function for tests
gof computes for a given dataset and based on the choices of the user either all tests for a given amount of copulae, performs for a given testset every test with all available copulae or computes for given copulae and tests all possible combinations.
gofRosenblattSnB contains the SnB gof test for copulae from Genest (2009) and compares the empirical copula against a parametric estimate of the copula derived under the null hypothesis. The margins can be estimated by a bunch of distributions and the time which is necessary for the estimation can be given. The approximate p-values are computed with a parametric bootstrap, which computation can be accelerated by enabling in-build parallel computation. The gof statistics are computed with the function gofTstat from the package copula. It is possible to insert datasets of all dimensions above 1 and the possible copulae are "normal", "t", "gumbel", "clayton" and "frank". The parameter estimation is performed with pseudo maximum likelihood method. In case the estimation fails, inversion of Kendall's tau is used.
gofKendallKS
(Package: gofCopula) :
gof test (Kolmogorov-Smirnof) based on Kendall's process
gofKendallKS tests a given dataset for a copula based on Kendall's process with the Kolmogorov-Smirnof test statistic. The margins can be estimated by a bunch of distributions and the time which is necessary for the estimation can be given. The possible copulae are "normal", "t", "gumbel", "clayton" and "frank". See for reference Genest et al. (2009). The parameter estimation is performed with pseudo maximum likelihood method. In case the estimation fails, inversion of Kendall's tau is used. The approximate p-values are computed with a parametric bootstrap, which computation can be accelerated by enabling in-build parallel computation.
gofPIOSRn
(Package: gofCopula) :
2 and 3 dimensional gof test based on the in-and-out-of-sample approach
gofPIOSRn tests a 2 or 3 dimensional dataset with the approximate PIOS test for a copula. The possible copulae are "normal", "t", "gumbel", "clayton" and "frank". The parameter estimation is performed with pseudo maximum likelihood method. In case the estimation fails, inversion of Kendall's tau is used. The approximate p-values are computed with a semiparametric bootstrap, which computation can be accelerated by enabling in-build parallel computation.
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