462 citations · 706 across the 37 of their papers we have counts for
42 papers
Spatial Aggregation of ROC and Precision-Recall Curves
Romain Pic, Zhongwei Zhang, Sebastian Engelke +1
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are widely used to assess the discrimination ability of forecasts for binary events, such as threshold exce…
Survival Isotonic Distributional Regression
Martin Bladt, Alexander Henzi, Bram van den Heuvel +1
We introduce Survival-IDR (S-IDR), a nonparametric estimator of conditional survival distributions under order restrictions, extending Isotonic Distributional Regression (IDR; Henz…
T-calibration in semi-parametric models
Anja Mühlemann, Johanna Ziegel
This note relates the calibration of models to the consistent loss functions for the target functional of the model. We demonstrate that a model is calibrated if and only if there…
Decompositions of the mean continuous ranked probability score
Sebastian Arnold, Eva-Maria Walz, Johanna Ziegel +1
The continuous ranked probability score (crps) is the most commonly used scoring rule in the evaluation of probabilistic forecasts for real-valued outcomes. To assess and rank fore…
Assessing the calibration of multivariate probabilistic forecasts
Sam Allen, Johanna Ziegel, David Ginsbourger
Rank and PIT histograms are established tools to assess the calibration of probabilistic forecasts. They not only check whether an ensemble forecast is calibrated, but they also re…
Isotonic conditional laws
Sebastian Arnold, Johanna Ziegel
We introduce isotonic conditional laws (ICL) which extend the classical notion of conditional laws by the additional requirement that there exists an isotonic relationship between…
Isotonic Recalibration under a Low Signal-to-Noise Ratio
Mario V. Wüthrich, Johanna Ziegel
Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts. Often, regression mode…
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,…
Sequentially valid tests for forecast calibration
Sebastian Arnold, Alexander Henzi, Johanna F. Ziegel
Forecasting and forecast evaluation are inherently sequential tasks. Predictions are often issued on a regular basis, such as every hour, day, or month, and their quality is monito…
Isotonic regression for functionals of elicitation complexity greater than one
Anja Mühlemann, Johanna F. Ziegel
We study the non-parametric isotonic regression problem for bivariate elicitable functionals that are given as an elicitable univariate functional and its Bayes risk. Prominent exa…
Valid sequential inference on probability forecast performance
Alexander Henzi, Johanna F. Ziegel
Probability forecasts for binary events play a central role in many applications. Their quality is commonly assessed with proper scoring rules, which assign forecasts a numerical s…
The Efficiency Gap
Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
Parameter estimation via M- and Z-estimation is equally powerful in semiparametric models for one-dimensional functionals due to a one-to-one relation between corresponding loss an…
Distributional (Single) Index Models
Alexander Henzi, Gian-Reto Kleger, Johanna F. Ziegel
A Distributional (Single) Index Model (DIM) is a semi-parametric model for distributional regression, that is, estimation of conditional distributions given covariates. The method…
Isotonic Distributional Regression
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. In a nutshell, IDR learns c…
Optimal solutions to the isotonic regression problem
Alexander I. Jordan, Anja Mühlemann, Johanna F. Ziegel
In general, the solution to a regression problem is the minimizer of a given loss criterion, and depends on the specified loss function. The nonparametric isotonic regression probl…
Evaluating Range Value at Risk Forecasts
Tobias Fissler, Johanna F. Ziegel
The debate of what quantitative risk measure to choose in practice has mainly focused on the dichotomy between Value at Risk (VaR) -- a quantile -- and Expected Shortfall (ES) -- a…