Copula-gebaseerde afhankelijkheid tussen stochastische vectoren en toepassingen

Contemporary data structures include multidimensional/multivariate data of different nature; data as numbers, curves, surfaces/images, graphs, and circular data. Flexible models are needed to adequately describe such data. This doctoral project focusses on statistical inference methods for the model parameters (finite or infinite-dimensional). The attention in this doctoral research is into the study of the complex dependence structures in the data, developing copula-like concepts. Different tools for dependence modelling are needed, depending on the data structures (e.g. graphical or circular). Inherent herein is the choice of a copula model and the marginal distributions. In a first step of the research the use of elements from information theory (such as entropy) combined with copula theory will be investigated. Among the aims is a detailed study of mutual information as copula-based measure of multivariate dependence.