Calibrating predictive distributions
Calibrating predictive distributions
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校准预测分布
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Vidoni
中科院分区：
文献类型：
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作者：
G. Fonseca;F. Giummolè;P. Vidoni
本文从频率论的观点讨论预测问题。目的是定义一个良好校准的预测分布，给出预测区间，特别是预测极限，覆盖概率等于或接近目标标称值。这种预测分布可以在许多情况下考虑，包括离散数据和非规则情况，并且它是建立在校准预测限制以控制相关覆盖概率的想法上的。每当计算所提出的分布是不可行的，这可以近似使用一个合适的自举模拟过程或考虑高阶渐近展开，给出预测分布已经在文献中已知。不同背景下的结果的例子和应用程序显示了广泛的适用性和所提出的预测分布的非常好的性能。
This paper concerns prediction from the frequentist point of view. The aim is to define a well-calibrated predictive distribution giving prediction intervals, and in particular prediction limits, with coverage probability equal or close to the target nominal value. This predictive distribution can be considered in a number of situations, including discrete data and non-regular cases, and it is founded on the idea of calibrating prediction limits to control the associated coverage probability. Whenever the computation of the proposed distribution is not feasible, this can be approximated using a suitable bootstrap simulation procedure or by considering high-order asymptotic expansions, giving predictive distributions already known in the literature. Examples and applications of the results to different contexts show the wide applicability and the very good performance of the proposed predictive distribution.