Publications

2019

• Brehmer, J. and Gneiting, T. (2019).
  Properization: Constructing proper scoring rules via Bayes acts.
  Annals of the Institute of Statistical Mathematics, 71, in press.
• Hemri, S. (2019).
  Multi-model combination and seamless prediction. 
  In: Handbook of Hydrometeorological Ensemble Forecasting.
  Duan, Q., Pappenberger, F., Wood, A., Cloke, H. L. and Schaake, J. C. (eds.).
  Springer, pp. 285-307.

2018

• Arnault, J., Rummler, T., Baur, F., Lerch, S., Wagner, S., Fersch, B., Zhang, Z., Kerandi, N., Keil, C. and 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.
• Baran, S. and Lerch, S. (2018).
  Combining predictive distributions for the statistical post-processing of ensemble forecasts.
  International Journal of Forecasting, 34, 477-496.
• Ehm, W. and Krüger, F. (2018).
  Forecast dominance testing via sign randomization.
  Electronic Journal of Statistics, 12, 3758-3793.
• Gneiting, T., Asher, J., Carriquiry, A., Davis, R., Dawid, A. P., Efron, B., Haberman, S., Kou, S., Newton, M., Paddock, S., Prewitt, K., Raftery, A., Stein, M. and 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.
• Pantillon, F., Lerch, S., Knippertz, P. and 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.
• Rasp, S. and Lerch, S. (2018).
  Neural networks for postprocessing ensemble weather forecasts.
  Monthly Weather Review, 146, 3885-3900.
• Vogel, P., Knippertz, P., Fink, A. H., Schlueter, A. and Gneiting, T. (2018).
  Skill of global raw and postprocessed ensemble predictions of rainfall over northern tropical Africa.
  Weather and Forecasting, 33, 369-388.

2017

• Ehm, W. and Ovcharov, E. Y. (2017).
  Bias-corrected score decomposition for generalized quantiles.
  Biometrika, 104, 473-480.
• Gneiting, T. (2017).
  When is the mode functional the Bayes classifier?
  Stat, 6, 204-206.
• Hemri, S. and Klein, B. (2017).
  Analog-based postprocessing of navigation-related hydrological ensemble forecasts.
  Water Resources Research, 53, 9059-9077.
• Krüger, F. (2017).
  Survey-based forecast distributions for Euro Area growth and inflation: Ensembles versus histograms.
  Empirical Economics, 53, 235-246.
• Krüger, F., Clark, T. E. and Ravazzolo, F. (2017).
  Using entropic tilting to combine BVAR forecasts with external nowcasts.
  Journal of Business & Economic Statistics, 35, 470-485.
• Lerch, S. and Baran, S. (2017).
  Similarity-based semilocal estimation of post-processing models.
  Journal of the Royal Statistical Society, Series C, 66, 29-51.
• Lerch, S., Thorarinsdottir, T. L., Ravazzolo, F. and Gneiting, T. (2017).
  Forecaster’s dilemma: Extreme events and forecast evaluation.
  Statistical Science, 32, 106-127.
• 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, 999-1008.
• Schmidt, P. (2017).
  Discussion of “Elicitability and backtesting: Perspectives for banking regulation”.
  Annals of Applied Statistics, 11, 1883-1885.

2016

• Baran, S. and Lerch, S. (2016).
  Mixture EMOS models for calibrating ensemble forecasts of wind speed.
  Environmetrics, 27, 116-130.
• Ehm, W. (2016).
  Reproducibility from the perspective of meta-analysis.
  In: Reproducibility: Principles, Problems, Practices, and Prospects.
  Atmanspacher, H. and Maasen, S. (eds.), pp. 141-167.
  Wiley, Hoboken.
• Ehm, W. and Wackermann, J. (2016).
  Geometric-optical illusions and Riemannian geometry.
  Journal of Mathematical Psychology, 71, 28-38.
• Ehm, W., Gneiting, T., Jordan, A. and Krüger, F. (2016).
  Of quantiles and expectiles: Consistent scoring functions, Choquet representations and forecast rankings.
  Journal of the Royal Statistical Society, Series B, 78, 505-562.
• Elliott, G., Ghanem, D. and Krüger, F. (2016).
  Forecasting conditional probabilities of binary outcomes under misspecification.
  Review of Economics and Statistics, 98, 742-755.
• Fissler, T., Ziegel, J. F. and Gneiting, T. (2016).
  Expected shortfall is jointly elicitable with value-at-risk: implications for backtesting.
  Risk Magazine, January 2016.
• Hemri, S., Haiden, T. and Pappenberger, F. (2016).
  Discrete post-processing of total cloud cover ensemble forecasts.
  Monthly Weather Review, 144, 2565-2577.
• Krüger, F. and Nolte, I. (2016).
  Disagreement versus uncertainty: Evidence from distribution forecasts.
  Journal of Banking & Finance, 72, 172-186.
• Schefzik, R. (2016a).
  A similarity-based implementation of the Schaake shuffle.
  Monthly Weather Review, 144, 1909-1921.
• Schefzik, R. (2016b).
  Combining parametric low-dimensional ensemble postprocessing with reordering methods.
  Quarterly Journal of the Royal Meteorological Society, 142, 2463-2477.

2015

• Baran, S. and Lerch, S. (2015).
  Log-normal distribution based EMOS models for probabilistic wind speed forecasting.
  Quarterly Journal of the Royal Meteorological Society, 141, 2289-2299.
• Feldmann, K., Thorarinsdottir, T. L. and Scheuerer, M. (2015).
  Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression.
  Monthly Weather Review, 143, 955-971.
• Hansen, L. V., Thorarinsdottir, T. L., Ovcharov, E., Gneiting, T. and Richards, D. (2015).
  Gaussian random particles with flexible Hausdorff dimension.
  Advances in Applied Probability, 47, 307-327.
• Hemri, S., Lisniak, D. and Klein, B. (2015).
  Multivariate postprocessing techniques for probabilistic hydrological forecasting.
  Water Resources Research, 51, 7436–7451.
• Ovcharov, E. Y. (2015).
  Existence and uniqueness of proper scoring rules.
  Journal of Machine Learning Research, 16, 2207-2230.
• Richardson, D., Hemri, S., Bogner, K., Gneiting, T., Haiden, T., Pappenberger, F. and Scheuerer, M. (2015).
  Calibration of ECMWF forecasts.
  ECMWF Newsletter, 142, 12-16.
• 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.

2014

• Dueck, J., Edelmann, D., Gneiting, T. and Richards, D. (2014).
  The affinely invariant distance correlation.
  Bernoulli, 20, 2305-2330.
• Gneiting, T. and Katzfuss, M. (2014).
  Probabilistic forecasting.
  Annual Review of Statistics and its Application, 1, 125-151.
• Hemri, S., Lisniak, D. and Klein, B. (2014).
  Ascertainment of probabilistic runoff forecasts considering censored data (in German).
  Hydrologie und Wasserbewirtschaftung, 58, 84-94.
• Hemri, S., Scheuerer, M., Pappenberger, F., Bogner, K. and Haiden, T. (2014).
  Trends in the predictive performance of raw ensemble weather forecasts.
  Geophysical Research Letters, 41, 9197-9205.
• Scheuerer, M. and Gneiting, T. (2014).
  Evaluating predictive performance.
  In: Mathematics of Planet Earth. Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences.
  Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J. J. and Vargas-Guzmán, J. A. (eds.), pp. 15-18.
  Springer, Berlin, Lecture Notes in Earth System Sciences.
• Ziegel, J. F. and Gneiting, T. (2014).
  Copula calibration.
  Electronic Journal of Statistics, 8, 2619-2638.

2013

• Gneiting, T. (2013).
  Strictly and non-strictly positive definite functions on spheres.
  Bernoulli, 19, 1327-1349.
• Gneiting, T. and Ranjan, R. (2013).
  Combining predictive distributions.
  Electronic Journal of Statistics, 7, 1747-1782.
• Grant, K. and 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
  Wellner. Institute of Mathematical Statistics Collections 9.
  Banerjee, M., Bunea, F., Huang, J., Koltchinskii, V. and Maathuis, M.H. (eds.), pp. 163-173.
  Institute of Mathematical Statistics, Beachwood.
• Schefzik, R., Thorarinsdottir, T. L. and Gneiting, T. (2013).
  Uncertainty quantification in complex simulation models using ensemble copula coupling.
  Statistical Science, 28, 616-640.
• Sloughter, J.M. Gneiting, T. and Raftery, A.E. (2013).
  Probabilistic wind vector forecasting using ensembles and Bayesian model averaging.
  Monthly Weather Review, 141, 2107-2119.
• Thorarinsdottir, T. L., Gneiting, T. and Gissibl, N. (2013).
  Using proper divergence functions to evaluate climate models. 
  SIAM/ASA Journal on Uncertainty Quantification, 1, 522-534.