Prof Claudia Czado (Technical University of Munich)
Fri 06 Sep 2019, 15:05 - 16:00
JCMB 5323

If you have a question about this talk, please contact: Serveh Sharifi Far (ssharifi)

Image for A D-Vine Copula-Based Model for Repeated Measurements

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 copula. We show that our approach is an extremely flexible extension of the widely used linear mixed model if the correlation is homogeneous over the considered individuals. As an alternative to joint maximum–likelihood a sequential estimation approach for the D-vine copula is provided and validated in a simulation study. The model can handle missing values without being forced to discard data. Since conditional distributions are known analytically, we easily make predictions for future events. For model selection, we adjust the Bayesian information criterion to our situation. In an application to heart surgery data our model performs clearly better than competing linear mixed models. This work is joint with Matthias Killiches.