Modul:   MAT959  Seminar in Data Science and Mathematical Modelling

Isotonic distributional regression and CRPS decompositions

Talk by Prof. Dr. Johanna Ziegel

Date: 23.05.24  Time: 12.15 - 13.45  Room: Y27H12

Isotonic distributional regression (IDR) is a nonparametric distributional regression approach under a monotonicity constraint. It has found application as a generic method for uncertainty quantification, in statistical postprocessing of weather forecasts, and in distributional single index models. IDR has favorable calibration and optimality properties in finite samples. Furthermore, it has an interesting population counterpart called isotonic conditional laws that generalize conditional distributions with respect to σ-algebras to conditional distributions with respect to σ-lattices. In this talk, an overview of the theory is presented. Furthermore, it is shown how IDR can be used to decompose the mean CRPS for assessing the predictive performance of models with regard to their calibration and discrimination ability.