In this paper, we propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the mean is related to a structured additive predictor. The latter is composed additively of a variety of...

In this paper, we propose a unified Bayesian approach for multivariate structured additive distributional regression analysis where inference is applicable to a huge class of multivariate response distributions, comprising continuous, discrete and latent models, and where each parameter of these...

To evaluate density forecasts, the applied scoring rule is often arbitrarily chosen. The selection of the scoring rule strongly influences the ranking of forecasts. This paper identifies overconfidence as the main driver for scoring differences. A novel approach to measure overconfidence is...

This paper proposes cost sensitive criteria for constructing classification rules by supervised learning methods. Reinterpreting established loss functions and considering those introduced by Buja, Stuetzle, et al. (2005) and Hand (2009), we identify criteria reflecting different degrees of...

A scoring rule is a reward function for eliciting or evaluating forecasts expressed as discrete or continuous probability distributions. A rule is strictly proper if it encourages the forecaster to state his true subjective probabilities, and effective if it is associated with a metric on the...