论文标题
全样本引导程序的一致性，用于估计高质量，尾巴概率和尾部索引
Consistency of full-sample bootstrap for estimating high-quantile, tail probability, and tail index
论文作者
论文摘要
我们表明，全样本引导程序对于为单变量分布的高质量，尾巴概率和其他尾巴参数构建置信区间渐近有效。这解决了对这种自举方法的有效性提出的疑问。在我们的广泛仿真研究中，自举法的总体性能要比标准渐近方法的总体性能要好，这表明Bootstrap方法至少比推论的渐近方法至少要好得多。本文还奠定了开发引导方法的基础，以推断多变量统计中的尾巴事件；这尤其重要，因为某些非启动方法很复杂。
We show that the full-sample bootstrap is asymptotically valid for constructing confidence intervals for high-quantiles, tail probabilities, and other tail parameters of a univariate distribution. This resolves the doubts that have been raised about the validity of such bootstrap methods. In our extensive simulation study, the overall performance of the bootstrap method was better than that of the standard asymptotic method, indicating that the bootstrap method is at least as good, if not better than, the asymptotic method for inference. This paper also lays the foundation for developing bootstrap methods for inference about tail events in multivariate statistics; this is particularly important because some of the non-bootstrap methods are complex.
