Fair scores for ensemble forecasts Fair Scores for Ensemble Forecasts
Fair scores for ensemble forecasts Fair Scores for Ensemble Forecasts
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集合预报的公平分数 集合预报的公平分数
DOI：
10.1002/qj.2270
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发表时间：
2014
影响因子：
8.9
通讯作者：
Ferro C
中科院分区：
文献类型：
--
作者：
Ferro C
集合预报的公平分数的概念是最近引入的，以奖励那些表现得好像它们和验证观测是从同一分布中采样的成员的集合。在预测二元结果的情况下，给出了被解释为随机样本的集合的一般类的公平分数的表征。这还用于为预测多类别和连续结果的集合构建公平分数类别。通常的布里尔，排名概率和连续排名概率分数集合预报被证明是不公平的，而这些分数的调整版本被证明是公平的。还提出了一个公平的定义合奏与成员被解释为依赖，它表明，公平的分数只存在于某些形式的依赖。
The notion of fair scores for ensemble forecasts was introduced recently to reward ensembles with members that behave as though they and the verifying observation are sampled from the same distribution. In the case of forecasting binary outcomes, a characterization is given of a general class of fair scores for ensembles that are interpreted as random samples. This is also used to construct classes of fair scores for ensembles that forecast multicategory and continuous outcomes. The usual Brier, ranked probability and continuous ranked probability scores for ensemble forecasts are shown to be unfair, while adjusted versions of these scores are shown to be fair. A definition of fairness is also proposed for ensembles with members that are interpreted as being dependent and it is shown that fair scores exist only for some forms of dependence.