Invited Keynote Speakers:
Universityof Wisconsin, Madison
Universityof Minnesota, Minneapolis
, Winner of the Tweedie Award Universityof Washington
Louis Chen, President of the IMS,
National Universityof Singapore
Speakers at Journal panel session:
Frank Samaniego, University of California, Davis, editor of JASA – Theory and Methods
Jim Albert, Bowling Green State University, editor of The American Statistician
Ed George, Wharton, University of Pennsylvania, editor of Statistical Science
Speakers at Funding panel session:
Bob Serfling, NSF
Terry Therneau, Mayo Clinic
A major human desire is to make forecasts for the future. Forecasts characterize and reduce but generally do not eliminate uncertainty. Consequently, forecasts should be probabilistic in nature, taking the form of probability distributions over future quantities or events.
The goal of probabilistic forecasting can be paraphrased as maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations, and is a joint property of the predictions and the events that materialize. Sharpness refers to the concentration of the predictive distributions, and is a property of the forecasts only. I will describe a game-theoretic framework and diagnostic tools for assessing calibration and sharpness, and I will discuss scoring rules that allow one to evaluate and rank probabilistic forecasters.
closes with a case study on probabilistic forecasts of wind resources at the
Stateline wind energy center in the
Joint work with Fadoua Balabdaoui, Adrian E. Raftery, Kristin Larson, Kenneth Westrick, Marc G. Genton and Eric Aldrich.