As copulas are frequently used to model dependence in statistical models, it is of central importance to be
able to accurately and efficiently sample from them. In the case of hierarchical Kendall copulas, a top-down sampling strategy involves simulation of a random vector given that it lies in a particular level set. While explicit solutions are available when hierarchical Kendall copulas are built from Archimedean copulas, this paper presents new results for the Plackett copula and for Archimax copulas, which also include the class of extreme-value copulas. Additionally, new approximate sampling procedures for hierarchical Kendall copulas are proposed and evaluated in a simulation study.
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As copulas are frequently used to model dependence in statistical models, it is of central importance to be
able to accurately and efficiently sample from them. In the case of hierarchical Kendall copulas, a top-down sampling strategy involves simulation of a random vector given that it lies in a particular level set. While explicit solutions are available when hierarchical Kendall copulas are built from Archimedean copulas, this paper presents new results for the Plackett copula and for Archim...
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