CST Group
Computational Statistics




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



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


  • S. Baran, S. Lerch (2015). Log-normal distribution based EMOS models for probabilistic wind speed forecasting, Quarterly Journal of the Royal Meteorological Society, 141:2289-2299 48
  • K. Feldmann, M. Scheuerer, T. L. Thorarinsdottir (2015). Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression, Monthly Weather Review, 143:955-971 50
  • L. V. Hansen, T. L. Thorarinsdottir, E. Ovcharov, T. Gneiting, D. Richards (2015). Gaussian random particles with flexible Hausdorff dimension, Advances in Applied Probability, 47:307-327 52
  • S. Hemri, D. Lisniak, B. Klein (2015). Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51:7436-7451 54
  • E. Ovcharov (2015). Existence and uniqueness of proper scoring rules, Journal of Machine Learning Research, 16:2207-2230 55
  • R. Schefzik (2015). Multivariate discrete copulas, with applications in probabilistic weather forecasting, Publications de’l Institut de Statistique de’l Université de Paris, 59:87116 57





  • Jochen Fiedler (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
  • Stephan Hemri (2016). Probabilistic Forecasting Based on Hydrometeorological Ensembles, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Uwe Ehret(Tutor), Tilmann Gneiting(HITS Tutor) 135
  • Alexander Jordan (2016). Facets of Forecast Evaluation, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Norbert Henze(Tutor), Tilmann Gneiting(HITS Tutor) 137
  • Sebastian Lerch (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

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