Robert L. Winkler

TL;DR: The results of a forecasting competition are presented to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.

TL;DR: This paper concerns the combination of experts' probability distributions in risk analysis, discussing a variety of combination methods and attempting to highlight the important conceptual and practical issues to be considered in designing a combination process in practice.

TL;DR: In this article, a family of scoring rules for the elicitation of continuous probability distributions are developed and discussed, which involve the computation of a score based on the assessor's stated probabilities and on the event that actually occurs.

TL;DR: In this paper, a general framework for forecast verification based on the joint distribution of forecasts and observations is described, and two factorizations of the joint distributions are investigated: 1) the calibration-refinement factorization, which involves the conditional distributions of observations given forecasts and the marginal distributions of forecasts, and 2) the likelihood-base factorization.

TL;DR: In this article, the authors investigate empirically the impact of the number and choice of forecasting methods on the accuracy of simple averages, and conclude that the forecasting accuracy improves, and that the variability of accuracy among different combinations decreases, as the number of methods in the average increases.