Probabilistic forecasts, calibration and sharpness
Probabilistic forecasts, calibration and sharpness
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DOI：
10.1111/j.1467-9868.2007.00587.x
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发表时间：
2007-01-01
影响因子：
5.8
通讯作者：
Raftery, Adrian E.
中科院分区：
文献类型：
--
作者：
Gneiting, Tilmann;Balabdaoui, Fadoua;Raftery, Adrian E.
连续变量的概率预测采用预测密度或预测累积分布函数的形式。我们提出了一种诊断方法的预测性能的评估是基于范式的预测分布的锐度最大化校准。校准是指分布预测和观测之间的统计一致性，是预测和实现的事件的共同属性。共享性是指预测分布的集中度，仅是预测的一个属性。一个简单的理论框架，使我们能够区分概率校准，重复校准和边际校准。我们提出并研究了用于检查校准和锐度的工具，其中包括概率积分变换直方图、边缘校准图、锐度图和适当的评分规则。诊断方法说明了在美国太平洋西北部的斯塔特林风能中心的风速概率预测的评估和排名。结合交叉验证或在时间序列的背景下，我们的建议提供了非常一般的，非参数的替代品，使用信息标准的模型诊断和模型选择。
Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of 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. A simple theoretical framework allows us to distinguish between probabilistic calibration, exceedance calibration and marginal calibration. We propose and study tools for checking calibration and sharpness, among them the probability integral transform histogram, marginal calibration plots, the sharpness diagram and proper scoring rules. The diagnostic approach is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest. In combination with cross-validation or in the time series context, our proposal provides very general, nonparametric alternatives to the use of information criteria for model diagnostics and model selection.