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28 papers · 1 filter
Learning to reflect: A unifying approach for data-driven stochastic control strategies
Sören Christensen, Claudia Strauch, Lukas Trottner
Stochastic optimal control problems have a long tradition in applied probability, with the questions addressed being of high relevance in a multitude of fields. Even though theoret…
Comparative evaluation of point process forecasts
Jonas Brehmer, Tilmann Gneiting, Marcus Herrmann +3
Stochastic models of point patterns in space and time are widely used to issue forecasts or assess risk, and often they affect societally relevant decisions. We adapt the concept o…
Mixing it up: A general framework for Markovian statistics
Niklas Dexheimer, Claudia Strauch, Lukas Trottner
Up to now, the nonparametric analysis of multidimensional continuous-time Markov processes has focussed strongly on specific model choices, mostly related to symmetry of the semigr…
Scoring Interval Forecasts: Equal-Tailed, Shortest, and Modal Interval
Jonas Brehmer, Tilmann Gneiting
We consider different types of predictive intervals and ask whether they are elicitable, i.e. are unique minimizers of a loss or scoring function in expectation. The equal-tailed i…
Why scoring functions cannot assess tail properties
Jonas Brehmer, Kirstin Strokorb
Motivated by the growing interest in sound forecast evaluation techniques with an emphasis on distribution tails rather than average behaviour, we investigate a fundamental questio…
Properization: Constructing Proper Scoring Rules via Bayes Acts
Jonas Brehmer, Tilmann Gneiting
Scoring rules serve to quantify predictive performance. A scoring rule is proper if truth telling is an optimal strategy in expectation. Subject to customary regularity conditions,…