CST Gruppe
Computational Statistics

Publikationen

2019

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, 19:1007–1025 338
  • Baran S, Lerch S (2018). Combining predictive distributions for the statistical post-processing of ensemble forecasts, International Journal of Forecasting, 34:477–496 339
  • Ehm W, Krüger F (2018). Forecast dominance testing via sign randomization, Electronic Journal of Statistics, 12:3758–3793 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: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, 144:1864–1881 342
  • Rasp S, Lerch S (2018). Neural networks for postprocessing ensemble weather forecasts, Monthly Weather Review, 146:3885–3900 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, 33:369–388 344

2017

2016

  • 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: Statistical Methodology, 78:505-562 49
  • Baran S, Lerch S (2016). Mixture EMOS models for calibrating ensemble forecasts of wind speed, Environmetrics, 27:116-130 128
  • Ehm W (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
  • 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 post-processing of total cloud cover ensemble forecasts, Monthly Weather Review, 144:2565-2577 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, 144:1909-1921 141
  • Schefzik R (2016). Combining parametric low-dimensional ensemble postprocessing with reordering methods, Quarterly Journal of the Royal Meteorological Society, 142:2463-2477 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 EMOS models for probabilistic wind speed forecasting, Quarterly Journal of the Royal Meteorological Society, 141:2289-2299 48
  • Feldmann K, Scheuerer M, Thorarinsdottir TL (2015). Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression, Monthly Weather Review, 143:955-971 50
  • Hansen L, Thorarinsdottir T, Ovcharov E, Gneiting T, Richards D (2015). Gaussian random particles with flexible Hausdorff dimension, Advances in Applied Probability, 47:307-327 52
  • Hemri S, Lisniak D, Klein B (2015). Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51:7436-7451 54
  • Ovcharov E (2015). Existence and uniqueness of proper scoring rules, Journal of Machine Learning Research, 16:2207-2230 55
  • Schefzik R (2015). Multivariate discrete copulas, with applications in probabilistic weather forecasting, Publications de’l Institut de Statistique de’l Université de Paris, 59:87116 57

2014

2013

2017

  • Ganster K (2017). Deterministic Forecasts of Binary Events: Comparison of Performance Measures, Faculty of Mathematics, Karlsruhe Institute of Technology, 2017, Tilmann Gneiting(Tutor) 224
  • Klar M (2017). Statistical Forecasts of Rain Occurrence over West Africa, Faculty of Mathematics, Karlsruhe Institute of Technology, 2017, Tilmann Gneiting(Tutor) 227

2016

  • Fiedler J (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
  • Gräter M (2016). Simulation Study of Dual Ensemble Copula Coupling, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Tilmann Gneiting(Tutor), Sebastian Lerch(HITS Tutor) 134
  • Hemri S (2016). Probabilistic Forecasting Based on Hydrometeorological Ensembles, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Uwe Ehret(Tutor), Tilmann Gneiting(HITS Tutor) 135
  • Jordan A (2016). Facets of Forecast Evaluation, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Norbert Henze(Tutor), Tilmann Gneiting(HITS Tutor) 137
  • Lerch S (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
  • Li L (2016). Assessing Point Forecasts — Economic and Statistical Measures, Faculty of Mathematics, Karlsruhe Institute of Technology, 2016, Tilmann Gneiting(Tutor), Fabian Krüger(HITS Tutor) 140

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