一种概率分布的极值分位数的最优估计
On Optimising the Estimation of Extreme Value Quantiles of a Probability Distribution
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摘要: 在本文中, 我们构造了一种新的极值分位数估计, 给出了估计量的极限性质. 同时, 在渐近二阶矩最小的准则下, 利用子样本自助法给出了计算所构造的极值分位数估计时的样本点分割方法, 从理论上证明了这一极限结果, 说明了这种分割在渐近二阶矩最小的准则下是渐近最优分割, 同时提出了自适应的样本点分割的自助算法.Abstract: In this paper, a new extreme value quantile estimator is given and its limit properties are discussed. Under the asymptotic second moment principle, recurring to sub-sample bootstrap method, the optimality problem of sample fraction in extreme value quantile estimation is solved, and the limit properties are proved. We prove our sample fraction is optimal under the asymptotic second moment principle, also an adaptive bootstrap procedure is given.