Publications

2019

• Kira Feldmann, David S. Richardson, Tilmann Gneiting (2019). Grid- versus station-based postprocessing of ensemble temperature forecasts, Geophys. Res. Lett. 46(13):7744-7751 1005
• Jonas R. Brehmer, Tilmann Gneiting (2019). Properization: Constructing proper scoring rules via Bayes acts, Ann Inst Stat Math 1004
• Stephan Hemri (2019). Multi-model Combination and Seamless Prediction, In Handbook of Hydrometeorological Ensemble Forecasting, Eds: Duan, Q. and Pappenberger, F. and Thielen, J. and Wood, A. and Cloke, H. L. and Schaake, J. C., Springer-Verlag, pp. 285-307 53
• Alexander Jordan, Fabian Krüger, Sebastian Lerch (2019). Evaluating probabilistic forecasts with scoringRules, J. Stat. Soft. 90(12) 1006

2018

• J. Arnault, T. Rummler, F. Baur, S. Lerch, S. Wagner, B. Fersch, Z. Zhang, N. Kerandi, C. Keil, H. Kunstmann (2018). Precipitation sensitivity to the uncertainty of terrestrial water flow in WRF-Hydro: An ensemble analysis for Central Europe, Journal of Hydrometeorology, 19:1007–1025 338
• S. Baran, S. Lerch (2018). Combining predictive distributions for the statistical post-processing of ensemble forecasts, International Journal of Forecasting, 34:477–496 339
• W. Ehm, F. Krüger (2018). Forecast dominance testing via sign randomization, Electronic Journal of Statistics, 12:3758–3793 340
• T. Gneiting, J. Asher, A. Carriquiry, R. Davis, A. P. Dawid, B. Efron, S. Haberman, S. Kou, M. Newton, S. Paddock, K. Prewitt, A. Raftery, M. Stein, M. Straf (2018). Special section in memory of Stephen E. Fienberg (1942–2016). AOAS Editor-in-Chief 2013–2015., Annals of Applied Statistics, 12:iii–x 341
• F. Pantillon, S. Lerch, P. Knippertz, U. Corsmeier (2018). Forecasting wind gusts in winter storms using a calibrated convection-permitting ensemble, Quarterly Journal of the Royal Meteorological Society, 144:1864–1881 342
• S. Rasp, S. Lerch (2018). Neural networks for postprocessing ensemble weather forecasts, Monthly Weather Review, 146:3885–3900 343
• P. Vogel, P. Knippertz, A. H. Fink, A. Schlueter, T. Gneiting (2018). Skill of global raw and postprocessed ensemble predictions of rainfall over northern tropical Africa, Weather and Forecasting, 33:369–388 344

2017

• Fabian Krüger (2017). Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms, Empir Econ 53(1):235-246 1076
• Fabian Krüger, Todd E. Clark, Francesco Ravazzolo (2017). Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts, Journal of Business & Economic Statistics 35(3):470-485 1077
• W. Ehm, E. Ovcharov (2017). Bias-corrected score decomposition for generalized quantiles, Biometrika, 104:473-480 223
• T. Gneiting (2017). When is the mode functional the Bayes classifier?, Stat, 6:204-206 225
• S. Hemri, B. Klein (2017). Analog-based postprocessing of navigation-related hydrological ensemble forecasts, Water Resources Research, 53:9059-9077 226
• S. Lerch, S. Baran (2017). Similarity-based semilocal estimation of post-processing models, Journal of the Royal Statistical Society Series: Applied Statistics, 66, :29-51 228
• S. Lerch, T. L. Thorarinsdottir, F. Ravazzolo, T. Gneiting (2017). Forecaster’s dilemma: Extreme events and forecast evaluation, Statistical Science, 32:106-127 229
• P. Schmidt (2017). Discussion of “Elicitability and backtesting: Perspectives for banking regulation”, Annals of Applied Statistics, 11:1883-1885 230
• Roman Schefzik (2017). Ensemble calibration with preserved correlations: unifying and comparing ensemble copula coupling and member-by-member postprocessing, Q.J.R. Meteorol. Soc. 143(703):999-1008 1078

2016

• Graham Elliott, Dalia Ghanem, Fabian Krüger (2016). Forecasting conditional probabilities of binary outcomes under misspecification, Review of Economics and Statistics 98(4):742-755 132
• W. Ehm, T. Gneiting, A. Jordan, F.. Krueger (2016). Of quantiles and expectiles: Consistent scoring functions, Choquet representations and forecast rankings (with discussion and reply), Journal of the Royal Statistical Society Series: Statistical Methodology, 78:505-562 49
• S. Baran, S. Lerch (2016). Mixture EMOS models for calibrating ensemble forecasts of wind speed, Environmetrics, 27:116-130 128
• W. Ehm (2016). Reproducibility from the perspective of meta-analysis, In Reproducibility: Principles, Problems, Practices, and Prospects, pp. 141-167, Eds: Atmanspacher, H. and Maasen, S., Wiley, Hoboken 129
• W. Ehm, J. Wackermann (2016). Geometric-optical illusions and Riemannian geometry, Journal of Mathematical Psychology, 71:28-38 130
• S. Hemri, T. Haiden, F. Pappenberger (2016). Discrete post-processing of total cloud cover ensemble forecasts, Monthly Weather Review, 144:2565-2577 136
• F. Krüger, I. Nolte (2016). Disagreement versus uncertainty: Evidence from distribution forecasts, Journal of Banking & Finance, 72:S172-S186 138
• R. Schefzik (2016). A similarity-based implementation of the Schaake shuffle, Monthly Weather Review, 144:1909-1921 141
• R. Schefzik (2016). Combining parametric low-dimensional ensemble postprocessing with reordering methods, Quarterly Journal of the Royal Meteorological Society, 142:2463-2477 142
• T. Fissler, J. F. Ziegel, T. Gneiting (2016). Expected shortfall is jointly elicitable with value-at-risk: Implications for backtesting, Risk Magazine, January:58-61 143

2015

• S. Baran, S. Lerch (2015). Log-normal distribution based EMOS models for probabilistic wind speed forecasting, Quarterly Journal of the Royal Meteorological Society, 141:2289-2299 48
• K. Feldmann, M. Scheuerer, T. L. Thorarinsdottir (2015). Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression, Monthly Weather Review, 143:955-971 50
• L. V. Hansen, T. L. Thorarinsdottir, E. Ovcharov, T. Gneiting, D. Richards (2015). Gaussian random particles with flexible Hausdorff dimension, Advances in Applied Probability, 47:307-327 52
• S. Hemri, D. Lisniak, B. Klein (2015). Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51:7436-7451 54
• E. Ovcharov (2015). Existence and uniqueness of proper scoring rules, Journal of Machine Learning Research, 16:2207-2230 55
• R. Schefzik (2015). Multivariate discrete copulas, with applications in probabilistic weather forecasting, Publications de’l Institut de Statistique de’l Université de Paris, 59:87116 57

2014

• S. Hemri, M. Scheuerer, F. Pappenberger, K. Bogner, T. Haiden (2014). Trends in the predictive performance of raw ensemble weather forecasts, Geophys. Res. Lett. 41(24):9197-9205 1081
• Johannes Dueck, Dominic Edelmann, Tilmann Gneiting, Donald Richards (2014). The affinely invariant distance correlation, Bernoulli 20(4):2305-2330 1079
• Stephan Hemri, Dmytro Lisniak, Bastian Klein (2014). Ermittlung probabilistischer Abflussvorhersagen unter Berücksichtigung zensierter Daten 1090
• Tilmann Gneiting, Matthias Katzfuss (2014). Probabilistic Forecasting, Annu. Rev. Stat. Appl. 1(1):125-151 1080
• D. Richardson, S. Hemri, K. Bogner, T. Gneiting, T. Haiden, F. Pappenberger, M. Scheuerer (2014). Calibration of ECMWF Forecasts, ECMWF Newsletter, 142:12-16, 2014/15 56
• Michael Scheuerer, Tilmann Gneiting (2014). Evaluating Predictive Performance, Mathematics of Planet Earth,pp.15-18,Springer Berlin Heidelberg 1082
• Johanna F. Ziegel, Tilmann Gneiting (2014). Copula calibration, Electron. J. Statist. 8(2):2619-2638 1083

2013

• Roman Schefzik, Thordis L. Thorarinsdottir, Tilmann Gneiting (2013). Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling, Statist. Sci. 28(4):616-640 1087
• Tilmann Gneiting (2013). Strictly and non-strictly positive definite functions on spheres, Bernoulli 19(4):1327-1349 1084
• J. McLean Sloughter, Tilmann Gneiting, Adrian E. Raftery (2013). Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging, Mon. Wea. Rev. 141(6):2107-2119 1088
• Tilmann Gneiting, Roopesh Ranjan (2013). Combining predictive distributions, Electron. J. Statist. 7(0):1747-1782 1085
• Kyrill Grant, Tilmann Gneiting (2013). Consistent scoring functions for quantiles, From Probability to Statistics and Back: High-Dimensional Models and Processes — A Festschrift in Honor of Jon A. Wellner,pp.163-173,Institute of Mathematical Statistics 1086
• Thordis L. Thorarinsdottir, Tilmann Gneiting, Nadine Gissibl (2013). Using Proper Divergence Functions to Evaluate Climate Models, SIAM/ASA J. Uncertainty Quantification 1(1):522-534 1089

Theses

2016

• Jochen Fiedler (2016). Of Graphs, Dimples, Distances, and Rotations: Linear and Non-linear Dependence Measures for Random Fields, Faculty of Mathematics and Informatics, Ruprecht-Karls University Heidelberg, 2016, Donald Richards(Tutor), Tilmann Gneiting(HITS Tutor) 133
• Stephan Hemri (2016). Probabilistic Forecasting Based on Hydrometeorological Ensembles, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Uwe Ehret(Tutor), Tilmann Gneiting(HITS Tutor) 135
• Alexander Jordan (2016). Facets of Forecast Evaluation, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Norbert Henze(Tutor), Tilmann Gneiting(HITS Tutor) 137
• Sebastian Lerch (2016). Probabilistic Forecasting and Comparative Model Assessment, With Focus on Extreme Events, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Thordis Thorarinsdottir, Vicky Fasen-Hartmann(Tutor), Tilmann Gneiting(HITS Tutor) 139