6 citations · 13 across the 5 of their papers we have counts for
6 papers
A D-vine copula based model for repeated measurements extending linear mixed models with homogeneous correlation structure
Matthias Killiches, Claudia Czado
We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D-vine co…
Using model distances to investigate the simplifying assumption, model selection and truncation levels for vine copulas
Matthias Killiches, Daniel Kraus, Claudia Czado
Vine copulas are a useful statistical tool to describe the dependence structure between several random variables, especially when the number of variables is very large. When modeli…
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…
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…
Model distances for vine copulas in high dimensions
Matthias Killiches, Daniel Kraus, Claudia Czado
Vine copulas are a flexible class of dependence models consisting of bivariate building blocks and have proven to be particularly useful in high dimensions. Classical model distanc…
Block-Maxima of Vines
Matthias Killiches, Claudia Czado
We examine the dependence structure of finite block-maxima of multivariate distributions. We provide a closed form expression for the copula density of the vector of the block-maxi…