The development and application of new statistical vine copula models

This thesis contributes to research in multivariate statistics by developing regular vine copula-based models that are more flexible and provide improved model fit. The main focus of the research is on mixture pair-copula based models as they can describe a range of multivariate dependency patterns. The research makes four main contributions related to the new models and provides mathematical and numerical results that showcase the advantages of the proposed approaches