Traditional quantile estimators that are based on one or two order statistics are a common way to estimate distribution quantiles based on the given samples. These estimators are robust, but their statistical efficiency is not always good enough. A more efficient alternative is the Harrell-Davis quantile estimator which uses a weighted sum of all order statistics. Whereas this approach provides more accurate estimations for the light-tailed distributions, it's not robust. To be able to customize the trade-off between statistical efficiency and robustness, we could consider a trimmed modification of the Harrell-Davis quantile estimator. In this approach, we discard order statistics with low weights according to the highest density interval of the beta distribution.

翻译：基于一两个顺序统计的传统定量估计器是根据给定样本估算分布量的常用方法。 这些估算器是稳健的, 但统计效率并不总是足够好。 一个更有效的替代办法是哈雷尔- 达维斯量化估计器, 使用所有排序统计的加权总和。 虽然这个方法为光零售分布提供了更准确的估算, 但并不可靠。 要能够定制统计效率和稳健之间的权衡, 我们可以考虑对哈雷尔- 达维斯定量估计器进行三重修改。 在这个方法中, 我们丢弃了根据贝塔分布密度最高间隔的低重量统计。