Publications

2023

• Resin J (2023). From classification accuracy to proper scoring rules: Elicitability of probabilistic top list predictions, Journal of Machine Learning Research, 24(173):1–21 1681
• Resin J (2023). A simple algorithm for exact multinomial tests, Journal of Computational and Graphical Statistics, 32(2):539–550 1504
• Bracher J, Koster N, Krüger F, Lerch S (2023). Learning to forecast: The Probabilistic Time Series Forecasting Challenge, The American Statistician, published online 1660
• Dimitriadis T, Liu X, Schnaitmann J (2023). Encompassing tests for value at risk and expected shortfall multistep forecasts based on inference on the boundary, Journal of Financial Econometrics, 21(2):412–444 1381
• Hoga Y, Dimitriadis T (2023). On testing equal conditional predictive ability under measurement error, Journal of Business & Economic Statistics, 41(2):364–376 1439
• Tempest KI, Craig GC, Brehmer JR (2023). Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble, Quarterly Journal of the Royal Meteorological Society, 149(752):677–702 1569
• Gneiting T, Wolffram D, Resin J, Kraus K, Bracher J, Dimitriadis T, Hagenmeyer V, Jordan AI, Lerch S, Phipps K, Schienle M (2023). Model diagnostics and forecast evaluation for quantiles, Annual Review of Statistics and Its Application, 10:597–621 1549
• Gneiting T, Lerch S, Schulz B (2023). Probabilistic solar forecasting: Benchmarks, post-processing, verification, Solar Energy, 252:72–80 1571

2022

• Bracher J, Wolffram D, Deuschel J, Görgen K, Ketterer JL, Ullrich A, Abbott S, Barbarossa MV, Bertsimas D, Bhatia S, Bodych M, Bosse NI, Burgard JP, Castro L, Fairchild G, Fiedler J, Fuhrmann J, Funk S, Gambin A, Gogolewski K, Heyder S, Hotz T, Kheifetz Y, Kirsten H, Krueger T, Krymova E, Leithäuser N, Li ML, Meinke JH, Miasojedow B, Michaud IJ, Mohring J, Nouvellet P, Nowosielski JM, Ożański T, Radwan M, Rakowski F, Scholz M, Soni S, Srivastava A, Gneiting T, Schienle M (2022). National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021, Communications Medicine, 2:136 1548
• Eisenstein L, Schulz B, Qadir GA, Pinto JG, Knippertz P (2022). Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies, Weather and Climate Dynamics, 3(4):1157–1182 1539
• Gneiting T, Walz E (2022). Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA), Machine Learning, 111:2769–2797 1375
• Cramer EY, Huang Y, Wang Y, Ray EL, Cornell M, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, House K, Jayawardena D, Kanji AH, Khandelwal A, Le K, Mody V, Mody V, Niemi J, Stark A, Shah A, Wattanchit N, Zorn MW, Reich NG, US COVID-19 Forecast Hub Consortium (2022). The United States COVID-19 Forecast Hub dataset, Scientific Data, 9:462 1510
• Bayer S, Dimitriadis T (2022). Regression-based expected shortfall backtesting, Journal of Financial Econometrics, 20(3):437–471 1405
• Gneiting T, Vogel P (2022). Receiver operating characteristic (ROC) curves: Equivalences, beta model, and minimum distance estimation, Machine Learning, 111:2147–2159 1421
• Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, Gerding A, Gneiting T, (282 further coauthors) , Walker JW, Slayton RB, Johansson MA, Biggerstaff M, Reich NG (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States, Proceedings of the National Academy of Sciences, 119(15):e2113561119 1475
• Jordan AI, Mühlemann A, Ziegel JF (2022). Characterizing the optimal solutions to the isotonic regression problem for identifiable functionals, Annals of the Institute of Statistical Mathematics, 74:489–514 1368
• Schulz B, Lerch S (2022). Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison, Monthly Weather Review, 150(1):235–257 1379

2021

• Bracher J, Wolffram D, Gneiting T, Schienle M (2021). Vorhersagen sind schwer, vor allem die Zukunft betreffend: Kurzzeitprognosen in der Pandemie, Mitteilungen der Deutschen Mathematiker-Vereinigung, 29(4):186–190 1429
• Brehmer J (2021). A construction principle for proper scoring rules, Proceedings of the American Mathematical Society, Series B, 8(24):297–301 1390
• Bracher J, Wolffram D, Deuschel J, Görgen K, Ketterer JL, Ullrich A, Abbott S, Barbarossa MV, Bertsimas D, Bhatia S, Bodych M, Bosse NI, Burgard JP, Castro L, Fairchild G, Fuhrmann J, Funk S, Gogolewski K, Gu Q, Heyder S, Hotz T, Kheifetz Y, Kirsten H, Krueger T, Krymova E, Li ML, Meinke JH, Michaud IJ, Niedzielewski K, Ożański T, Rakowski F, Scholz M, Soni S, Srivastava A, Zieliński J, Zou D, Gneiting T, Schienle M (2021). A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave, Nature Communications, 12:5173 1367
• Henzi A, Ziegel JF, Gneiting T (2021). Isotonic distributional regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83(5):963–993 1369
• Schmidt P, Katzfuss M, Gneiting T (2021). Interpretation of point forecasts with unknown directive, Journal of Applied Econometrics, 36(6):728–743 1370
• Walz E, Maranan M, van der Linden R, Fink AH, Knippertz P (2021). An IMERG-based optimal extended probabilistic climatology (EPC) as a benchmark ensemble forecast for precipitation in the tropics and subtropics, Weather and Forecasting, 36(4):1561–1573 1420
• Whan K, Zscheischler J, Jordan AI, Ziegel JF (2021). Novel multivariate quantile mapping methods for ensemble post-processing of medium-range forecasts, Weather and Climate Extremes, 32:100310 1371
• Vassella CC, Koch J, Henzi A, Jordan A, Waeber R, Iannaccone R, Charrière R (2021). From spontaneous to strategic natural window ventilation: Improving indoor air quality in Swiss schools, International Journal of Hygiene and Environmental Health, 234:113746 1363
• Brehmer JR, Gneiting T (2021). Scoring interval forecasts: Equal-tailed, shortest, and modal interval, Bernoulli, 27(3):1993–2010 1372
• Schulz B, Ayari ME, Lerch S, Baran S (2021). Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting, Solar Energy, 220:1016–1031 1380
• Dimitriadis T, Schnaitmann J (2021). Forecast encompassing tests for the expected shortfall, International Journal of Forecasting, 37(2):604–621 1373
• Dimitriadis T, Gneiting T, Jordan AI (2021). Stable reliability diagrams for probabilistic classifiers, Proceedings of the National Academy of Sciences, 118(8):e2016191118 1374
• Bracher J, Ray EL, Gneiting T, Reich NG (2021). Evaluating epidemic forecasts in an interval format, PLOS Computational Biology, 17(2):e1008618 1406
• Vogel P, Knippertz P, Gneiting T, Fink AH, Klar M, Schlueter A (2021). Statistical forecasts for the occurrence of precipitation outperform global models over northern tropical Africa, Geophysical Research Letters, 48(3):e2020GL091022 1407
• Baran Á, Lerch S, Ayari ME, Baran S (2021). Machine learning for total cloud cover prediction, Neural Computing and Applications, 33(7):2605–2620 1388
• Krüger F, Lerch S, Thorarinsdottir T, Gneiting T (2021). Predictive inference based on Markov chain Monte Carlo output, International Statistical Review, 89(2):274–301 1409

2020

• Brehmer JR, Gneiting T (2020). Properization: Constructing proper scoring rules via Bayes acts, Annals of the Institute of Statistical Mathematics, 72:659–673 1004
• Ziegel JF, Krüger F, Jordan A, Fasciati F (2020). Robust forecast evaluation of expected shortfall, Journal of Financial Econometrics,18(1):95–120 1404
• Vogel P, Knippertz P, Fink AH, Schlueter A, Gneiting T (2020). Skill of global raw and postprocessed ensemble predictions of rainfall in the tropics, Weather and Forecasting, 35(6):2367–2385 1410

2019

• Feldmann K, Richardson DS, Gneiting T (2019). Grid- versus station-based postprocessing of ensemble temperature forecasts, Geophysical Research Letters, 46(13):7744–7751 1005
• Hemri S (2019). Multi-model combination and seamless prediction, In Handbook of Hydrometeorological Ensemble Forecasting, Eds: Duan Q, Pappenberger F, Thielen J, Wood A, Cloke HL, Schaake JC, Springer-Verlag, pp. 285–307 53
• Jordan A, Krüger F, Lerch S (2019). Evaluating probabilistic forecasts with scoringRules, Journal of Statistical Software, 90(12):1–37 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(6):1007–1025 338
• Baran S, Lerch S (2018). Combining predictive distributions for the statistical post-processing of ensemble forecasts, International Journal of Forecasting, 34(3):477–496 339
• Ehm W, Krüger F (2018). Forecast dominance testing via sign randomization, Electronic Journal of Statistics, 12(2):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(2):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(715):1864–1881 342
• Rasp S, Lerch S (2018). Neural networks for postprocessing ensemble weather forecasts, Monthly Weather Review, 146(11):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(2):369–388 344

2017

• Ehm W, Ovcharov E (2017). Bias-corrected score decomposition for generalized quantiles, Biometrika, 104(2):473–480 223
• Gneiting T (2017). When is the mode functional the Bayes classifier?, Stat, 6(1):204–206 225
• Hemri S, Klein B (2017). Analog-based postprocessing of navigation-related hydrological ensemble forecasts, Water Resources Research, 53(11):9059–9077 226
• Lerch S, Baran S (2017). Similarity-based semilocal estimation of post-processing models, Journal of the Royal Statistical Society: Series C (Applied Statistics), 66(1):29–51 228
• Lerch S, Thorarinsdottir TL, Ravazzolo F, Gneiting T (2017). Forecaster’s dilemma: Extreme events and forecast evaluation, Statistical Science, 32(1):106–127 229
• Schmidt P (2017). Discussion of “Elicitability and backtesting: Perspectives for banking regulation”, Annals of Applied Statistics, 11(4):1883–1885 230
• Krüger F (2017). Survey-based forecast distributions for Euro Area growth and inflation: Ensembles versus histograms, Empirical Economics, 53(1):235–246 1391
• 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 1392
• Schefzik R (2017). Ensemble calibration with preserved correlations: Unifying and comparing ensemble copula coupling and member‐by‐member postprocessing, Quarterly Journal of the Royal Meteorological Society, 143(703):999–1008 1393

2016

• Ehm W (2016). Reproducibility from the perspective of meta-analysis, In Reproducibility: Principles, Problems, Practices, and Prospects, Eds: Atmanspacher H, Maasen S, Wiley, Hoboken, pp. 141–167 129
• 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 B (Statistical Methodology), 78(3):505–562 49
• Baran S, Lerch S (2016). Mixture EMOS model for calibrating ensemble forecasts of wind speed, Environmetrics, 27(2):116–130 128
• 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 postprocessing of total cloud cover ensemble forecasts, Monthly Weather Review, 144(7):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(5):1909–1921 141
• Schefzik R (2016). Combining parametric low‐dimensional ensemble postprocessing with reordering methods, Quarterly Journal of the Royal Meteorological Society, 142(699):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 ensemble model output statistics models for probabilistic wind‐speed forecasting, Quarterly Journal of the Royal Meteorological Society, 141(691):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(3):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(2):307–327 52
• Hemri S, Lisniak D, Klein B (2015). Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9):7436–7451 54
• Ovcharov E (2015). Existence and uniqueness of proper scoring rules, Journal of Machine Learning Research, 16(67):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, 59:87–116 57

2014

• Hemri S, Lisniak D, Klein B (2014). Ermittlung probabilistischer Abflussvorhersagen unter Berücksichtigung zensierter Daten, Hydrologie und Wasserbewirtschaftung, 58(2):84–94 1090
• Richardson D, Hemri S, Bogner K, Gneiting T, Haiden T, Pappenberger F, Scheuerer M (2014). Calibration of ECMWF forecasts, ECMWF Newsletter, 142:12–16 56
• Hemri S, Scheuerer M, Pappenberger F, Bogner K, Haiden T (2014). Trends in the predictive performance of raw ensemble weather forecasts, Geophysical Research Letters, 41(24):9197–9205 1394
• Gneiting T, Katzfuss M (2014). Probabilistic forecasting, Annual Review of Statistics and Its Application, 1(1):125–151 1395
• Scheuerer M, Gneiting T (2014). Evaluating predictive performance, In Mathematics of Planet Earth, Lecture Notes in Earth System Sciences, Eds: Pardo-Igúzquiza E, Guardiola-Albert C, Heredia J, Moreno-Merino L, Durán J, Vargas-Guzmán J, Springer, Berlin, Heidelberg, pp. 15–18 1396
• Ziegel JF, Gneiting T (2014). Copula calibration, Electronic Journal of Statistics, 8(2):2619–2638 1397
• Dueck J, Edelmann D, Gneiting T, Richards D (2014). The affinely invariant distance correlation, Bernoulli, 20(4):2305–2330 1411

2013

• Schefzik R, Thorarinsdottir TL, Gneiting T (2013). Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28(4):616–640 1398
• Sloughter JM, Gneiting T, Raftery AE (2013). Probabilistic wind vector forecasting using ensembles and Bayesian model averaging, Monthly Weather Review, 141(6):2107–2119 1400
• Gneiting T, Ranjan R (2013). Combining predictive distributions, Electronic Journal of Statistics, 7:1747–1782 1401
• Thorarinsdottir TL, Gneiting T, Gissibl N (2013). Using proper divergence functions to evaluate climate models, SIAM/ASA Journal on Uncertainty Quantification,1(1):522–534 1402
• Grant K, Gneiting T (2013). Consistent scoring functions for quantiles, In From Probability to Statistics and Back: High-Dimensional Models and Processes — A Festschrift in Honor of Jon A. Wellner, Eds: Banerjee M, Bunea F, Huang J, Koltchinskii V, Maathuis MH, Institute of Mathematical Statistics, pp. 163–173 1403
• Gneiting T (2013). Strictly and non-strictly positive definite functions on spheres, Bernoulli,19(4):1327–1349 1412

2022

• Feldmann K (2022). Aspects of spatial postprocessing for global temperature forecasts (Doctoral Thesis), PhD Thesis, School of Business Informatics and Mathematics, University of Mannheim 1570

2020

• Brehmer JR (2020). Theory and methodology of scoring functions: Tail properties, interval forecasts, and point processes (Doctoral Thesis), PhD Thesis, School of Business Informatics and Mathematics, University of Mannheim 1343

2019

• Vogel P (2019). Assessing predictive performance: From precipitation forecasts over the tropics to receiver operating characteristic curves and back (Doctoral Thesis), PhD Thesis, Faculty of Mathematics, Karlsruhe Institute of Technology 1336

2016

• Fiedler J (2016). Distances, Gegenbauer expansions, curls, and dimples: On dependence measures for random fields (Doctoral Thesis), PhD Thesis, Faculty of Mathematics and Computer Science, Heidelberg University 133
• Hemri S (2016). Probabilistic forecasting based on hydrometeorological ensembles (Doctoral Thesis), PhD Thesis, Faculty of Mathematics, Karlsruhe Institute of Technology 135
• Jordan A (2016). Facets of forecast evaluation (Doctoral Thesis), PhD Thesis, Faculty of Mathematics, Karlsruhe Institute of Technology 137
• Lerch S (2016). Probabilistic forecasting and comparative model assessment, with focus on extreme events (Doctoral Thesis), PhD Thesis, Faculty of Mathematics, Karlsruhe Institute of Technology 139

2015

• Schefzik R (2015). Physically coherent probabilistic weather forecasts using multivariate discrete copula-based ensemble postprocessing methods (Doctoral Thesis), PhD Thesis, Faculty of Mathematics and Computer Science, Heidelberg University 1415