A Gibbs sampler for a Poisson regression model including spatial effects is presented and evaluated. The approach is based on that a Poisson regression model can be transformed into an approximate normal linear model by data augmentation using the introduction of two sequences of latent...

Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate...

Joint modeling of multiple health related random variables is essential to develop an understanding for the public health consequences of an aging population. This is particularly true for patients suffering from multiple chronic diseases. The contribution is to introduce a novel model for...

Operational risk data, when available, are usually scarce, heavy-tailed and possibly dependent. In this work, we introduce a model that captures such real-world characteristics and explicitly deals with heterogeneous pairwise and tail dependence of losses. By considering flexible families of...

Vine pair-copula constructions (PCCs) provide an important milestone for the usage of multivariate copulas to model dependence. At present time PCCs are recognized to be the most flexible class of multivariate copulas. Vine PCCs and semiparametric copula-based dynamic (SCOMDY) models with...

Multivariate discrete response data can be found in diverse fields, including econometrics, finance, biometrics, and psychometrics. Our contribution, through this study, is to introduce a new class of models for multivariate discrete data based on pair copula constructions (PCCs) that has two...

Misperceptions about extreme dependencies between different financial assets have been an important element of the recent financial crisis, which is why regulating entities do now require financial institutions to account for different behavior under market stress. Such sudden switches in...