World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

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

Tilmann Gneiting publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Tilmann Gneiting sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 136 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Tilmann Gneiting D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Tilmann Gneiting sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 60 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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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