Censored Data and the Bootstrap
本文研究在数据存在右删失时，如何为未知分布参数设定标准误和置信区间，并利用自助法（一种刀切法的扩展）提供通用解决方案，通过实际数据、计算机模拟和理论分析验证其有效性。
This article concerns setting standard errors and confidence intervals for the parameters of an unknown distribution when the data is subject to right censoring. The bootstrap, which is an elaboration of the jackknife, provides a general method for answering such questions. The validity of bootstrap methods is investigated using real data, computer simulations, and, in the final section, brief theoretical considerations.