论文标题
计算预测间隔的方法：审核和新结果
Methods to Compute Prediction Intervals: A Review and New Results
论文作者
论文摘要
本文在参数框架下回顾了两种主要类型的预测间隔方法。首先，我们描述了基于（近似）关键量的方法。示例包括插件，关键和校准方法。然后，我们根据预测分布（有时是基于可能性得出的）来描述方法。示例包括贝叶斯，信托和直接引导方法。提供了几个涉及连续分布的示例以及模拟研究以评估覆盖范围概率。我们在不同的预测间隔方法之间为（log-）位置尺度分布族提供了特定的连接。本文还使用二项式和泊松分布作为示例讨论了离散数据的一般预测间隔方法。例如，我们还概述了有关依赖数据的方法，例如，应用时间序列，空间数据和马尔可夫随机字段。
This paper reviews two main types of prediction interval methods under a parametric framework. First, we describe methods based on an (approximate) pivotal quantity. Examples include the plug-in, pivotal, and calibration methods. Then we describe methods based on a predictive distribution (sometimes derived based on the likelihood). Examples include Bayesian, fiducial, and direct-bootstrap methods. Several examples involving continuous distributions along with simulation studies to evaluate coverage probability properties are provided. We provide specific connections among different prediction interval methods for the (log-)location-scale family of distributions. This paper also discusses general prediction interval methods for discrete data, using the binomial and Poisson distributions as examples. We also overview methods for dependent data, with application to time series, spatial data, and Markov random fields, for example.
