462 citations · 706 across the 37 of their papers we have counts for
9 papers · 1 filter
Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-valued Model Output
Eva-Maria Walz, Alexander Henzi, Johanna Ziegel +1
How can we quantify uncertainty if our favorite computational tool - be it a numerical, a statistical, or a machine learning approach, or just any computer model - provides single-…
Weighted verification tools to evaluate univariate and multivariate forecasts for high-impact weather events
Sam Allen, Jonas Bhend, Olivia Martius +1
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts i…
E-backtesting
Qiuqi Wang, Ruodu Wang, Johanna Ziegel
In the recent Basel Accords, the Expected Shortfall (ES) replaces the Value-at-Risk (VaR) as the standard risk measure for market risk in the banking sector, making it the most imp…
Characterizing M-estimators
Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evalu…
Osband's Principle for Identification Functions
Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identif…
Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures
Johanna Ziegel, David Ginsbourger, Lutz Dümbgen
We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic. In particular, we discuss radial kernel…
Anytime-valid sequential testing for elicitable functionals via supermartingales
Philippe Casgrain, Martin Larsson, Johanna Ziegel
We design sequential tests for a large class of nonparametric null hypotheses based on elicitable and identifiable functionals. Such functionals are defined in terms of scoring fun…
Honest calibration assessment for binary outcome predictions
Timo Dimitriadis, Lutz Duembgen, Alexander Henzi +2
Probability predictions from binary regressions or machine learning methods ought to be calibrated: If an event is predicted to occur with probability , it should materialize wi…
A safe Hosmer-Lemeshow test
Alexander Henzi, Marius Puke, Timo Dimitriadis +1
This article proposes an alternative to the Hosmer-Lemeshow (HL) test for evaluating the calibration of probability forecasts for binary events. The approach is based on e-values,…