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Casgrain, Philippe; Larsson, Martin; Ziegel, Johanna (2024). Anytime-valid sequential testing for elicitable functionals via supermartingales. Bernoulli, 30(2), pp. 1347-1374. International Statistical Institute 10.3150/23-BEJ1634
Arnold, Sebastian; El Barmi, Hammou; Mukerjee, Hari; Ziegel, Johanna (2024). Estimating several survival functions under uniform stochastic ordering. Statistics and Probability Letters, 208 Elsevier 10.1016/j.spl.2024.110045
Dimitriadis, Timo; Fissler, Tobias; Ziegel, Johanna (2024). Osband’s principle for identification functions. Statistical Papers, 65(2), pp. 1125-1132. Springer 10.1007/s00362-023-01428-x
Ziegel, Johanna; Ginsbourger, David; Dümbgen, Lutz (2024). Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures. Bernoulli, 30(2), pp. 1441-1457. International Statistical Institute 10.3150/23-BEJ1639
Dimitriadis, Timo; Dümbgen, Lutz; Henzi, Alexander; Puke, Marius; Ziegel, Johanna (2023). Honest calibration assessment for binary outcome predictions. Biometrika, 110(3), pp. 663-680. Oxford University Press 10.1093/biomet/asac068
Allen, Sam; Ginsbourger, David; Ziegel, Johanna (2023). Evaluating Forecasts for High-Impact Events Using Transformed Kernel Scores. SIAM/ASA Journal on Uncertainty Quantification, 11(3), pp. 906-940. Society for Industrial and Applied Mathematics 10.1137/22M1532184
Dimitriadis, Timo; Fissler, Tobias; Ziegel, Johanna (2023). Characterizing M-estimators. Biometrika, 111(1), pp. 339-346. Oxford University Press 10.1093/biomet/asad026
Wüthrich, Mario V.; Ziegel, Johanna (2023). Isotonic recalibration under a low signal-to-noise ratio. Scandinavian actuarial journal, pp. 1-21. Taylor & Francis 10.1080/03461238.2023.2246743
Henzi, Alexander; Puke, Marius; Dimitriadis, Timo; Ziegel, Johanna (2023). A safe Hosmer-Lemeshow test. The New England journal of statistics in data science New England Statistical Society 10.51387/23-NEJSDS56
Allen, Sam; Bhend, Jonas; Martius, Olivia; Ziegel, Johanna (11 September 2022). Weighted verification tools to evaluate univariate and multivariate forecasts for high-impact weather events (arXiv). Cornell University
