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This paper addresses the problem of efficiently sampling exchangeable and nested Archimedean copulas, with specific focus on large dimensions, where methods involving generator derivatives, such as the conditional distribution method, are not applicable. Additionally, new conditions under which Archimedean copulas can be mixed to construct nested(More)
The package copula (formerly nacopula) provides procedures for constructing nested Archimedean copulas in any dimensions and with any kind of nesting structure, generating vectors of random variates from the constructed objects, computing function values and probabilities of falling into hypercubes, as well as evaluation of characteristics such as Kendall's(More)
Statistical inference for copulas has been addressed in various research papers. Due to the complicated theoretical results, studies have been carried out mainly in the bivariate case, be it properties of estimators or goodness-of-fit tests. However, from a practical point of view, higher dimensions are of interest. This work presents the results of(More)