On prediction intervals based on predictive likelihood or bootstrap methods.
On prediction intervals based on predictive likelihood or bootstrap methods.
复制标题
基于预测可能性或引导方法的预测区间。
DOI：
--
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
N. Tajvidi
中科院分区：
文献类型：
--
作者：
P. Hall;L. Peng;N. Tajvidi
我们认为，预测区间的基础上预测的可能性不正确的曲率相对于参数值时，他们隐式地近似一个未知的概率密度。部分由于这种困难，与预测区间和预测限相关的覆盖误差的顺序仅等于样本大小的倒数。在这方面，这些方法并没有改进更简单的、“天真的”或“估计的”方法。此外，在具有实际重要性的情况下，就覆盖误差的大小和符号而言，后者可能是优选的。我们表明，引导校准的天真和预测似然方法增加了一个数量级的预测区间的覆盖精度，并在天真的间隔的情况下，保留该方法的数值和分析的简单性。因此，我们认为，引导校准的天真的方法是一个特别有竞争力的替代更传统的，但更复杂的，基于预测的可能性的技术。
We argue that prediction intervals based on predictive likelihood do not correct for curvature with respect to the parameter value when they implicitly approximate an unknown probability density. Partly as a result of this difficulty, the order of coverage error associated with predictive intervals and predictive limits is equal to only the inverse of sample size. In this respect those methods do not improve on the simpler, 'naive' or 'estimative' approach. Moreover, in cases of practical importance the latter can be preferable, in terms of both the size and sign of coverage error. We show that bootstrap calibration of both naive and predictive-likelihood approaches increases coverage accuracy of prediction intervals by an order of magnitude, and, in the case of naive intervals, preserves that method's numerical and analytical simplicity. Therefore, we argue, the bootstrap-calibrated naive approach is a particularly competitive alternative to more conventional, but more complex, techniques based on predictive likelihood.