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stat.AP2016
Vine copula based likelihood estimation of dependence patterns in multivariate event time data
Nicole Barthel, Candida Geerdens, Matthias Killiches +2
In many studies multivariate event time data are generated from clusters having a possibly complex association pattern. Flexible models are needed to capture this dependence. Vine…
stat.AP2016
Examination and visualisation of the simplifying assumption for vine copulas in three dimensions
Matthias Killiches, Daniel Kraus, Claudia Czado
Vine copulas are a highly flexible class of dependence models, which are based on the decomposition of the density into bivariate building blocks. For applications one usually make…