论文标题
从块数据中推断高尾部分布的高分位数
Inference of high quantiles of a heavy-tailed distribution from block data
论文作者
论文摘要
在本文中，当在块中仅观察到少数最大的值时，我们考虑了从块数据中的高尾部分布的高分位数的估计问题。我们提出了高分位数的估计器，并证明这些估计器在渐近上是正常的。此外，我们采用经验可能性方法和调整后的经验可能性方法来构建高分位数的置信区间。通过仿真研究，我们还比较了正常近似方法的性能以及根据置信区间的覆盖概率和时间长度的调整后的经验可能性方法。
In this paper we consider the estimation problem for high quantiles of a heavy-tailed distribution from block data when only a few largest values are observed within blocks. We propose estimators for high quantiles and prove that these estimators are asymptotically normal. Furthermore, we employ empirical likelihood method and adjusted empirical likelihood method to constructing the confidence intervals of high quantiles. Through a simulation study we also compare the performance of the normal approximation method and the adjusted empirical likelihood methods in terms of the coverage probability and length of the confidence intervals.
