On Bootstrap Resampling and Iteration
提出一种统一的自助法重抽样方法，适用于广泛的统计问题，能聚焦于覆盖误差、区间长度或偏差等关键特征，并自然导出通用的自助法迭代形式。
We propose a single unifying approach to bootstrap resampling, applicable to a very wide range of statistical problems. It enables attention to be focused sharply on one or more characteristics which are of major importance in any particular problem, such as coverage error or length for confidence intervals, or bias for point estimation. Our approach leads easily and directly to a very general form of bootstrap iteration, unifying and generalizing present disparate accounts of this subject. It also provides simple solutions to relatively complex problems, such as a suggestion by Lehmann (1986) for ‘conditionally’ short confidence intervals.