Forecasts for unknown future events are ubiquitous. Since decisions of great impact are based on such forecasts, objective forecast evaluation and comparison are of utmost importance. We are conducting research on decision theoretically sound methods for forecast evaluation and comparison. This often also leads to new methods for the modeling of random phenomena and for data analysis. Recent progress in this direction concerns distributional regression under order constraints. Furthermore, there are connections to other areas such as risk measures in mathematical finance. We consider applications in areas such as meteorology, finance and medicine.

The group is involved in several national and international collaborations on a variety of topics. In particular, there is close exchange with the Computational Statistics group at the Heidelberg Institute of Theoretical Studies (HITS) in Germany headed by Prof. Tilmann Gneiting (HITS and KIT).