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Mathematics

D-Index
60
Citations
27100
World Ranking
536
National Ranking
26

Research.com Recognitions

  • 2017 - Fellow of the American Statistical Association (ASA)

Overview

Tilmann Gneiting is affiliated with the Heidelberg Institute for Theoretical Studies in Germany. Their research spans several intersecting fields, primarily focused on mathematics, with significant contributions in management science and operations research, statistics and probability, atmospheric science, modeling and simulation, and global and planetary change.

Their work covers a variety of topics, including forecasting techniques and applications, COVID-19 epidemiological studies, meteorological phenomena and simulations, data-driven disease surveillance, climate variability and models, advanced statistical methods and models, and precipitation measurement and analysis.

Tilmann Gneiting has published extensively in numerous academic venues. Frequent publication outlets include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), PLoS Computational Biology, Proceedings of the National Academy of Sciences, and the Electronic Journal of Statistics.

Recent notable papers include:

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States, 2022, Proceedings of the National Academy of Sciences
  • Evaluating epidemic forecasts in an interval format, 2021, PLoS Computational Biology
  • The United States COVID-19 Forecast Hub dataset, 2022, Scientific Data
  • A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave, 2021, Nature Communications
  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US, 2021, bioRxiv (Cold Spring Harbor Laboratory)

Coauthors frequently collaborating with Tilmann Gneiting include Johannes Bracher, Ajitesh Srivastava, Eva-Maria Walz, Daniel Wolffram, and Geoffrey Fairchild.

In recognition of professional contributions, Tilmann Gneiting was named Fellow of the American Statistical Association in 2017.

Best Publications

  • Strictly Proper Scoring Rules, Prediction, and Estimation

    Tilmann Gneiting;Adrian E Raftery

  • Using Bayesian Model Averaging to Calibrate Forecast Ensembles

    Adrian E. Raftery;Tilmann Gneiting;Fadoua Balabdaoui;Michael Polakowski

  • Probabilistic forecasts, calibration and sharpness

    Tilmann Gneiting;Fadoua Balabdaoui;Adrian E. Raftery

  • Making and Evaluating Point Forecasts

    Tilmann Gneiting

  • Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

    Tilmann Gneiting;Adrian E. Raftery;Anton H. Westveld;Tom Goldman

  • Nonseparable, Stationary Covariance Functions for Space–Time Data

    Tilmann Gneiting

  • Weather Forecasting with Ensemble Methods

    Tilmann Gneiting;Adrian E. Raftery

  • Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules

    Tilmann Gneiting;Roopesh Ranjan

  • Stochastic Models That Separate Fractal Dimension and the Hurst Effect

    Tilmann Gneiting;Martin Schlather

  • Predictive model assessment for count data.

    Claudia Czado;Tilmann Gneiting;Leonhard Held

  • Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

    J. Mc Lean Sloughter;Adrian E. Raftery;Tilmann Gneiting;Chris Fraley

  • Matérn Cross-Covariance Functions for Multivariate Random Fields

    Tilmann Gneiting;William Kleiber;Martin Schlather

  • Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry

    Tilmann Gneiting;Marc G. Genton;Peter Guttorp

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

    Unknown

  • Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data

    Tilmann Gneiting;Hana Ševčíková;Donald B. Percival

  • Stochastic models which separate fractal dimension and Hurst effect

    Tilmann Gneiting;Martin Schlather

  • Strictly and non-strictly positive definite functions on spheres

    Tilmann Gneiting

  • Compactly Supported Correlation Functions

    Tilmann Gneiting

  • Studies in the history of probability and statistics XLIX On the Matérn correlation family

    Peter Guttorp;Tilmann Gneiting

  • Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression

    Thordis L. Thorarinsdottir;Tilmann Gneiting

  • Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

    J. McLean Sloughter;Tilmann Gneiting;Adrian E. Raftery

  • Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

    Tilmann Gneiting;Kristin Larson;Kenneth Westrick;Marc G Genton

Frequent Co-Authors

Adrian E. Raftery
Adrian E. Raftery University of Washington
Peter Knippertz
Peter Knippertz Karlsruhe Institute of Technology
Andreas H. Fink
Andreas H. Fink Karlsruhe Institute of Technology
Marc G. Genton
Marc G. Genton King Abdullah University of Science and Technology
Leonhard Held
Leonhard Held University of Zurich
Clifford F. Mass
Clifford F. Mass University of Washington
Jana Sillmann
Jana Sillmann Universität Hamburg
John A. Pyle
John A. Pyle University of Cambridge
Axel Munk
Axel Munk University of Göttingen
Claudia Czado
Claudia Czado Technical University of Munich

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