Prof. Dr. Tilmann Gneiting

2022

• Gneiting T, Wolffram D, Resin J, Kraus K, Bracher J, Dimitriadis T, Hagenmeyer V, Jordan AI, Lerch S, Phipps K, Schienle M (2022). Model diagnostics and forecast evaluation for quantiles, Annual Review of Statistics and Its Application, published online 1549
• 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
• 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
• 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

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
• 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
• Brehmer JR, Gneiting T (2021). Scoring interval forecasts: Equal-tailed, shortest, and modal interval, Bernoulli, 27(3):1993–2010 1372
• 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
• 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
• 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
• 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
• 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

2018

• 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
• 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

• Gneiting T (2017). When is the mode functional the Bayes classifier?, Stat, 6(1):204–206 225
• Lerch S, Thorarinsdottir TL, Ravazzolo F, Gneiting T (2017). Forecaster’s dilemma: Extreme events and forecast evaluation, Statistical Science, 32(1):106–127 229

2016

• 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
• 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

• 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

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
• 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