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 Panel of the Journal of the Royal Statistical Society: Series B (Statistical Methodology).Publications
Click here for a listing of my published and accepted papers.
Recent Technical Reports
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.
Berrocal, V., Raftery, A. E. and Gneiting, T. (2008). Probabilistic quantitative precipitation forecasting using a two-stage spatial model. University of Washington, Department of Statistics, Technical Report no. 532.
Czado, C., Gneiting, T. and Held, L. (2007). Predictive model assessment for count data. University of Washington, Department of Statistics, Technical Report no. 518.
Fraley, C., Raftery, A. E., Sloughter, J. M. and Gneiting, T. (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.
Integration and visualization of multi-source information for mesoscale meteorology: Statistical and cognitive approaches to visualizing uncertainty. Joint with Adrian Raftery (PI, Statistics), Cliff Mass (Atmospheric Sciences), Earl Hunt and Susan Joslyn (Psychology), Robert Miyamoto, David Jones and Scott Sandgathe (Applied Physics Laboratory). Supported by the DoD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research.
Check our probcast real-time probabilistic weather forecasting website for the Pacific Northwest.
Some Recent Presentations
Bayesian model averaging and the geostatistical output perturbation technique: Statistical approaches to probabilistic weather forecasting. Session on Statistics and the Environment, IMS Annual Meeting, Institute of Mathematical Statistics, Rio de Janeiro (Brazil), 1 August 2006.
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.
Statistics 581, Autumn Quarter 2007.
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Statistics 582, Winter Quarter 2008.
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Last modified 30 April 2008
Statistics 581/582 (Advanced Theory of Statistical Inference)