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- Author or Editor: Carl-Axel S. Staël von Holstein x
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Abstract
Probabilistic forecasts were given for temperature and precipitation for 2 and 5-day periods. They were evaluated by a scoring rule and the scores were given as feedback to the participants together with the actual outcomes. On the average the forecasts did not improve on forecasts based on climatology, and the study indicates some explanations for this. It also shows how the meteorologists' knowledge could be used to produce probabilistic forecasts that are better than climatology. The study also covers empirical evaluation of different scoring rules and of techniques for aggregating sets of forecasts.
Abstract
Probabilistic forecasts were given for temperature and precipitation for 2 and 5-day periods. They were evaluated by a scoring rule and the scores were given as feedback to the participants together with the actual outcomes. On the average the forecasts did not improve on forecasts based on climatology, and the study indicates some explanations for this. It also shows how the meteorologists' knowledge could be used to produce probabilistic forecasts that are better than climatology. The study also covers empirical evaluation of different scoring rules and of techniques for aggregating sets of forecasts.
Abstract
An assessment which is “further away” from the “true” event than another assessment should receive a lower score. A definition of distance (from the true event) is considered as well as the sensitivity of scoring rules to distance. A family of scoring rules for n × n cost-loss ratio decision situations with incomplete knowledge is defined and is shown to be strictly proper. These scoring rules are modified to give a family of strictly proper scoring rules which are also sensitive to distance.
Abstract
An assessment which is “further away” from the “true” event than another assessment should receive a lower score. A definition of distance (from the true event) is considered as well as the sensitivity of scoring rules to distance. A family of scoring rules for n × n cost-loss ratio decision situations with incomplete knowledge is defined and is shown to be strictly proper. These scoring rules are modified to give a family of strictly proper scoring rules which are also sensitive to distance.
Abstract
A family of quadratic scoring rules (QSR's) is defined. Some properties of these scoring rules are described, and it is demonstrated that QSR's are strictly proper. The probability (or Brier) score and the ranked probability score are shown to be special cases of the general QSR.
A geometrical framework for the representation of QSR's is presented. This framework facilitates formulation of QSR's and provides insight into the properties of these scoring rules, including the sensitive-to-distance property. The relationships between QSR's and measures of the value of (probability) forecasts are briefly discussed.
The richness of the family of QSR's provides the evaluator with considerable flexibility in choosing a scoring rule that is particularly suited to the situation at hand.
Abstract
A family of quadratic scoring rules (QSR's) is defined. Some properties of these scoring rules are described, and it is demonstrated that QSR's are strictly proper. The probability (or Brier) score and the ranked probability score are shown to be special cases of the general QSR.
A geometrical framework for the representation of QSR's is presented. This framework facilitates formulation of QSR's and provides insight into the properties of these scoring rules, including the sensitive-to-distance property. The relationships between QSR's and measures of the value of (probability) forecasts are briefly discussed.
The richness of the family of QSR's provides the evaluator with considerable flexibility in choosing a scoring rule that is particularly suited to the situation at hand.
Abstract
A geometrical framework for the representation of cumulative forecasts and observations is described. The ranked probability score is shown to be the square of the distance between the points in this framework which represent a cumulative forecast and the relevant cumulative observation. The relationship between this framework and the geometrical framework for the probability score is indicated.
Abstract
A geometrical framework for the representation of cumulative forecasts and observations is described. The ranked probability score is shown to be the square of the distance between the points in this framework which represent a cumulative forecast and the relevant cumulative observation. The relationship between this framework and the geometrical framework for the probability score is indicated.
Abstract
Some forecasters apparently subscribe to a model of the subjective probability forecasting process in which their judgments are expressed in terms of “second-order” probabilities. First, we briefly consider the nature of these second-order probabilities and describe the second-order model, and then we demonstrate that strictly proper scoring rules encourage forecasters who subscribe to the second-order model to make their forecasts correspond to their expected judgments.
Abstract
Some forecasters apparently subscribe to a model of the subjective probability forecasting process in which their judgments are expressed in terms of “second-order” probabilities. First, we briefly consider the nature of these second-order probabilities and describe the second-order model, and then we demonstrate that strictly proper scoring rules encourage forecasters who subscribe to the second-order model to make their forecasts correspond to their expected judgments.
