Efron Bradley, Narasimhan Balasubramanian
Department of Biomedical Data Sciences and Department of Statistics Stanford University.
J Comput Graph Stat. 2020;29(3):608-619. doi: 10.1080/10618600.2020.1714633. Epub 2020 Mar 12.
The standard intervals, e.g., for nominal 95% two-sided coverage, are familiar and easy to use, but can be of dubious accuracy in regular practice. Bootstrap confidence intervals offer an order of magnitude improvement-from first order to second order accuracy. This paper introduces a new set of algorithms that automate the construction of bootstrap intervals, substituting computer power for the need to individually program particular applications. The algorithms are described in terms of the underlying theory that motivates them, along with examples of their application. They are implemented in the R package bcaboot.
标准区间，例如用于名义95%双侧覆盖的区间，为人所熟知且易于使用，但在常规实践中其准确性可能存疑。自助置信区间提供了一个数量级的改进——从一阶精度提升到二阶精度。本文介绍了一组新的算法，这些算法可自动构建自助区间，用计算机算力替代了针对特定应用进行单独编程的需求。文中根据激发这些算法的基础理论对其进行了描述，并给出了应用示例。它们在R包bcaboot中得以实现。
