More Efficient Bootstrap Computations
本文提出比标准蒙特卡洛更高效的Bootstrap计算方法，适用于单样本非参数问题，可大幅减少所需重抽样次数，并提供诊断方法判断适用性。
Abstract This article concerns computational methods for the bootstrap that are more efficient than the straightforward Monte Carlo methods usually used. The bootstrap is considered in its simplest form: in a one-sample nonparametric problem, where the goal is to estimate the bias or variance of some statistic by bootstrap sampling, or to set approximate confidence intervals for a parameter of interest in terms of various percentiles of the bootstrap distribution. The methods of this article can, in favorable situations, reduce the necessary number of bootstrap replications manyfold. Moreover, simple diagnostics are available to see whether or not any particular case is accessible to these methods.