Tilmann Gneiting
Tilmann Gneiting is Professor in the Department of Statistics at the University of Washington, Seattle. His research interests center on statistical methodology and probability theory, with applications in atmospheric, environmental and geophysical sciences, and economics, among other disciplines. In particular, he has worked on probabilistic weather prediction and wind energy forecasting. Tilmann is a recipient of a National Science Foundation Career Award (2002-2007) and of the Institute of Mathematical Statistics' Inaugural Richard L. Tweedie New Researcher Award (2005). He is a member of the Editorial Panels of the Annals of Applied Statistics, Environmetrics, the Journal of the Royal Statistical Society Series B (Statistical Methodology) and Weather and Forecasting.Publications
Click here for a listing of my published and accepted papers.
Recent Technical Reports
Ehm, W. and Gneiting, T. (2009). Local proper scoring rules. University of Washington, Department of Statistics, Technical Report no. 551.
Gneiting, T., Kleiber, W. and Schlather, M. (2009). Matérn cross-covariance functions for multivariate random fields. University of Washington, Department of Statistics, Technical Report no. 549.
Thorarinsdottir, T. L. and Gneiting, T. (2008). Probabilistic forecasts of wind speed: Ensemble model output statistics using heteroskedastic censored regression. University of Washington, Department of Statistics, Technical Report no. 546.
Sloughter, J. M., Gneiting, T. and Raftery, A. E. (2008). Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. University of Washington, Department of Statistics, Technical Report no. 544.
Ranjan, R. and Gneiting, T. (2008). Combining probability forecasts. University of Washington, Department of Statistics, Technical Report no. 543.
Gneiting, T. (2008). Quantiles as optimal point predictors. University of Washington, Department of Statistics, Technical Report no. 538.
Gneiting, T. and Ranjan, R. (2008). Comparing density forecasts using threshold and quantile weighted scoring rules. University of Washington, Department of Statistics, Technical Report no. 533.
Fraley, C., Raftery, A. E., Gneiting, T. and Sloughter, J. M. (2007). ensembleBMA: An R package for probabilistic forecasting using ensembles and Bayesian model averaging. University of Washington, Department of Statistics, Technical Report no. 516.
Berrocal, V., Raftery, A. E., Gneiting, T. and Steed, R. (2007). Probabilistic weather forecasting for winter road maintenance. University of Washington, Department of Statistics, Technical Report no. 511.
Current Research Projects
Proper scoring rules, calibration and sharpness: Assessing predictions for an uncertain world. Supported by the National Science Foundation.
A multidisciplinary approach to communicating weather forecast uncertainty. Joint with Susan Joslyn (PI, Psychology), Adrian Raftery (Statistics), Cliff Mass (Atmospheric Sciences) and David Jones (Applied Physics Laboratory). Supported by the National Science Foundation.
Visit our probcast real-time probabilistic weather forecasting website for the Pacific Northwest.
Some Recent Presentations
Recent developments in spatio-temporal geostatistics. StatGIS Summer School, Klagenfurt (Austria), 15 September 2006.
Strictly proper scoring rules: Assessing
predictions for an uncertain world. Session on Scoring Rules:
Eliciting and Evaluating Forecasters' Probabilities, INFORMS Annual
Meeting, Institute for Operations Research and the Management
Sciences, Seattle, Washington, 6 November 2007.
Last modified 6 February 2009