Publications

2019

• Feldmann K, Richardson DS, Gneiting T (2019). Grid- versus station-based postprocessing of ensemble temperature forecasts, Geophys. Res. Lett. 46(13):7744-7751 1005
• Brehmer JR, Gneiting T (2019). Properization: Constructing proper scoring rules via Bayes acts, Ann Inst Stat Math 1004
• Hemri S (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
• Jordan A, Krüger F, Lerch S (2019). Evaluating probabilistic forecasts with scoringRules, J. Stat. Soft. 90(12) 1006

2018

• Arnault J, Rummler T, Baur F, Lerch S, Wagner S, Fersch B, Zhang Z, Kerandi N, Keil C, Kunstmann H (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
• Baran S, Lerch S (2018). Combining predictive distributions for the statistical post-processing of ensemble forecasts, International Journal of Forecasting, 34:477–496 339
• Ehm W, Krüger F (2018). Forecast dominance testing via sign randomization, Electronic Journal of Statistics, 12:3758–3793 340
• Gneiting T, Asher J, Carriquiry A, Davis R, Dawid AP, Efron B, Haberman S, Kou S, Newton M, Paddock S, Prewitt K, Raftery A, Stein M, Straf M (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
• Pantillon F, Lerch S, Knippertz P, Corsmeier U (2018). Forecasting wind gusts in winter storms using a calibrated convection-permitting ensemble, Quarterly Journal of the Royal Meteorological Society, 144:1864–1881 342
• Rasp S, Lerch S (2018). Neural networks for postprocessing ensemble weather forecasts, Monthly Weather Review, 146:3885–3900 343
• Vogel P, Knippertz P, Fink AH, Schlueter A, Gneiting T (2018). Skill of global raw and postprocessed ensemble predictions of rainfall over northern tropical Africa, Weather and Forecasting, 33:369–388 344

2017

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

• Elliott G, Ghanem D, Krüger F (2016). Forecasting conditional probabilities of binary outcomes under misspecification, Review of Economics and Statistics 98(4):742-755 132
• Ehm W, Gneiting T, Jordan A, Krueger F (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
• Baran S, Lerch S (2016). Mixture EMOS models for calibrating ensemble forecasts of wind speed, Environmetrics, 27:116-130 128
• Ehm W (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
• Ehm W, Wackermann J (2016). Geometric-optical illusions and Riemannian geometry, Journal of Mathematical Psychology, 71:28-38 130
• Hemri S, Haiden T, Pappenberger F (2016). Discrete post-processing of total cloud cover ensemble forecasts, Monthly Weather Review, 144:2565-2577 136
• Krüger F, Nolte I (2016). Disagreement versus uncertainty: Evidence from distribution forecasts, Journal of Banking & Finance, 72:S172-S186 138
• Schefzik R (2016). A similarity-based implementation of the Schaake shuffle, Monthly Weather Review, 144:1909-1921 141
• Schefzik R (2016). Combining parametric low-dimensional ensemble postprocessing with reordering methods, Quarterly Journal of the Royal Meteorological Society, 142:2463-2477 142
• Fissler T, Ziegel JF, Gneiting T (2016). Expected shortfall is jointly elicitable with value-at-risk: Implications for backtesting, Risk Magazine, January:58-61 143

2015

• Baran S, Lerch S (2015). Log-normal distribution based EMOS models for probabilistic wind speed forecasting, Quarterly Journal of the Royal Meteorological Society, 141:2289-2299 48
• Feldmann K, Scheuerer M, Thorarinsdottir TL (2015). Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression, Monthly Weather Review, 143:955-971 50
• Hansen L, Thorarinsdottir T, Ovcharov E, Gneiting T, Richards D (2015). Gaussian random particles with flexible Hausdorff dimension, Advances in Applied Probability, 47:307-327 52
• Hemri S, Lisniak D, Klein B (2015). Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51:7436-7451 54
• Ovcharov E (2015). Existence and uniqueness of proper scoring rules, Journal of Machine Learning Research, 16:2207-2230 55
• Schefzik R (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

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

2013

• Schefzik R, Thorarinsdottir TL, Gneiting T (2013). Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling, Statist. Sci. 28(4):616-640 1087
• Gneiting T (2013). Strictly and non-strictly positive definite functions on spheres, Bernoulli 19(4):1327-1349 1084
• Sloughter JM, Gneiting T, Raftery AE (2013). Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging, Mon. Wea. Rev. 141(6):2107-2119 1088
• Gneiting T, Ranjan R (2013). Combining predictive distributions, Electron. J. Statist. 7(0):1747-1782 1085
• Grant K, Gneiting T (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
• Thorarinsdottir TL, Gneiting T, Gissibl N (2013). Using Proper Divergence Functions to Evaluate Climate Models, SIAM/ASA J. Uncertainty Quantification 1(1):522-534 1089

2017

• Ganster K (2017). Deterministic Forecasts of Binary Events: Comparison of Performance Measures, Faculty of Mathematics, Karlsruhe Institute of Technology, 2017, Tilmann Gneiting(Tutor) 224
• Klar M (2017). Statistical Forecasts of Rain Occurrence over West Africa, Faculty of Mathematics, Karlsruhe Institute of Technology, 2017, Tilmann Gneiting(Tutor) 227

2016

• Fiedler J (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
• Gräter M (2016). Simulation Study of Dual Ensemble Copula Coupling, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Tilmann Gneiting(Tutor), Sebastian Lerch(HITS Tutor) 134
• Hemri S (2016). Probabilistic Forecasting Based on Hydrometeorological Ensembles, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Uwe Ehret(Tutor), Tilmann Gneiting(HITS Tutor) 135
• Jordan A (2016). Facets of Forecast Evaluation, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Norbert Henze(Tutor), Tilmann Gneiting(HITS Tutor) 137
• Lerch S (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
• Li L (2016). Assessing Point Forecasts — Economic and Statistical Measures, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Tilmann Gneiting(Tutor), Fabian Krüger(HITS Tutor) 140