no code implementations • 25 Jan 2023 • Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan, Peter Vogel
Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand.
no code implementations • 7 Aug 2020 • Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams.
5 code implementations • 26 May 2020 • Johannes Bracher, Evan L. Ray, Tilmann Gneiting, Nicholas G. Reich
For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current COVID-19 pandemic are issued in the form of central predictive intervals at various levels.
Applications Populations and Evolution
2 code implementations • 29 Nov 2019 • Tilmann Gneiting, Eva-Maria Walz
Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems.
3 code implementations • 9 Sep 2019 • Alexander Henzi, Johanna F. Ziegel, Tilmann Gneiting
Isotonic distributional regression (IDR) is a powerful nonparametric technique for the estimation of conditional distributions under order restrictions.
Methodology Statistics Theory Statistics Theory
no code implementations • 24 Aug 2016 • Fabian Krüger, Sebastian Lerch, Thordis L. Thorarinsdottir, Tilmann Gneiting
Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output.
Methodology
no code implementations • 27 Mar 2015 • Werner Ehm, Tilmann Gneiting, Alexander Jordan, Fabian Krüger
We show that any scoring function that is consistent for a quantile or an expectile functional, respectively, can be represented as a mixture of extremal scoring functions that form a linearly parameterized family.