Publikationen
2025 2024 Nag P, Hong Y, Abdulah S, Qadir GA, Genton MG, Sun Y (2024). Efficient large-scale nonstationary spatial covariance function estimation using convolutional neural networks , Journal of Computational and Graphical Statistics, published online 1868 Walz E, Knippertz P, Fink AH, Köhler G, Gneiting T (2024). Physics-based vs. data-driven 24-hour probabilistic forecasts of precipitation for northern tropical Africa , Monthly Weather Review, 152(9):2011–2031 1845 Dimitriadis T, Gneiting T, Jordan AI, Vogel P (2024). Evaluating probabilistic classifiers: The triptych , International Journal of Forecasting, 40(3):1101–1122 1735 Lopez VK, Cramer EY, Pagano R, Drake JM, O’Dea EB, Adee M, Ayer T, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller PP, Xiao J, Bracher J, Castro Rivadeneira AJ, Gerding A, Gneiting T, Huang Y, Jayawardena D, Kanji AH, Le K, Mühlemann A, Niemi J, Ray EL, Stark A, Wang Y, Wattanachit N, Zorn MW, Pei S, Shaman J, Yamana TK, Tarasewicz SR, Wilson DJ, Baccam S, Gurung H, Stage S, Suchoski B, Gao L, Gu Z, Kim M, Li X, Wang G, Wang L, Wang Y, Yu S, Gardner L, Jindal S, Marshall M, Nixon K, Dent J, Hill AL, Kaminsky J, Lee EC, Lemaitre JC, Lessler J, Smith CP, Truelove S, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Karlen D, Castro L, Fairchild G, Michaud IJ, Osthus D, Bian J, Cao W, Gao Z, Lavista Ferres J, Li C, Liu T, Xie X, Zhang S, Zheng S, Chinazzi M, Davis JT, Mu K, Pastory y Piontti A, Vespignani A, Xiong X, Walraven R, Chen J, Gu Q, Wang L, Xu P, Zhang W, Zou D, Gibson GC, Sheldon D, Srivastava A, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Peddireddy AS, Porebski P, Venkatramanan S, Wang L, Prasad PV, Walker JW, Webber AE, Slayton RB, Biggerstaff M, Reich NG, Johansson MA (2024). Challenges of COVID-19 case forecasting in the US, 2020–2021 , PLOS Computational Biology, 20(5):e1011200 1843 Fosten J, Gutknecht D, Pohle M (2024). Testing quantile forecast optimality , Journal of Business & Economic Statistics, 42(4):1367–1378 1786 Horat N, Lerch S (2024). Deep learning for postprocessing global probabilistic forecasts on subseasonal time scales , Monthly Weather Review, 152(3):667–687 1825 Song M, Yang D, Lerch S, Xia X, Yagli GM, Bright JM, Shen Y, Liu B, Liu X, Mayer MJ (2024). Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts , Advances in Atmospheric Sciences, 41(7):1417–1437 1826 Walz E, Henzi A, Ziegel J, Gneiting T (2024). Easy uncertainty quantification (EasyUQ): Generating predictive distributions from single-valued model output , SIAM Review, 66(1):91–122 1782 Bracher J, Koster N, Krüger F, Lerch S (2024). Learning to forecast: The Probabilistic Time Series Forecasting Challenge , The American Statistician, 78(1):115–127 1660 Brehmer JR, Gneiting T, Herrmann M, Marzocchi W, Schlather M, Strokorb K (2024). Comparative evaluation of point process forecasts , Annals of the Institute of Statistical Mathematics, 76:47–71 1692 2023 Brockhaus EK, Wolffram D, Stadler T, Osthege M, Mitra T, Littek JM, Krymova E, Klesen AJ, Huisman JS, Heyder S, Helleckes LM, an der Heiden M, Funk S, Abbott S, Bracher J (2023). Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany , PLOS Computational Biology, 19(11):e1011653 1811 Rasheeda Satheesh A, Knippertz P, Fink AH, Walz E, Gneiting T (2023). Sources of predictability of synoptic‐scale rainfall during the West African summer monsoon , Quarterly Journal of the Royal Meteorological Society, 149(757):3721–3737 1720 Bracher J, Rüter L, Krüger F, Lerch S, Schienle M (2023). Direction augmentation in the evaluation of armed conflict predictions , International Interactions, 49(6):989–1004 1810 Bosse NI, Abbott S, Cori A, van Leeuwen E, Bracher J, Funk S (2023). Scoring epidemiological forecasts on transformed scales , PLOS Computational Biology, 19(8):e1011393 1721 Wolffram D, Abbott S, an der Heiden M, Funk S, Günther F, Hailer D, Heyder S, Hotz T, van de Kassteele J, Küchenhoff H, Müller-Hansen S, Syliqi D, Ullrich A, Weigert M, Schienle M, Bracher J (2023). Collaborative nowcasting of COVID-19 hospitalization incidences in Germany , PLOS Computational Biology, 19(8):e1011394 1722 Ray EL, Brooks LC, Bien J, Biggerstaff M, Bosse NI, Bracher J, Cramer EY, Funk S, Gerding A, Johansson MA, Rumack A, Wang Y, Zorn M, Tibshirani RJ, Reich NG (2023). Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States , International Journal of Forecasting, 39(3):1366–1383 1691 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 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 Gneiting T, Resin J (2023). Regression diagnostics meets forecast evaluation: Conditional calibration, reliability diagrams, and coefficient of determination , Electronic Journal of Statistics, 17(2):3226–3286 1813 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 Schanzer S, Koch M, Kiefer A, Jentke T, Veith M, Bracher F, Bracher J, Müller C (2022). Analysis of pesticide and persistent organic pollutant residues in German bats , Chemosphere, 305:135342 1812 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 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 Feldmann K, Richardson DS, Gneiting T (2019). Grid- versus station-based postprocessing of ensemble temperature forecasts , Geophysical Research Letters, 46(13):7744–7751 1005 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 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 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 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 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, Lisniak D, Klein B (2014). Ermittlung probabilistischer Abflussvorhersagen unter Berücksichtigung zensierter Daten , Hydrologie und Wasserbewirtschaftung, 58(2):84–94 1090 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 2020 2019 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