Symmetric Quantile Averages and Related Estimators
提出了对称分位数均值和组合分位数均值两类估计量，前者效率、稳健性和计算性能良好，并能导出精确的非参数位置置信区间；后者效率与稳健性更优，可与15%修整均值媲美，且同样能构造精确非参数置信区间。
The average of two complementary order statistics is termed a symmetric quantile average. In addition to having quite good efficiency, robustness and computational properties, exact nonparametric confidence intervals for location can be derived from it. A related type of estimator called a combined quantile average is introduced; it is slightly more difficult to compute than the symmetric quantile average, but can have better efficiency and robustness properties. In fact the recommended form of combined quantile average is comparable for robustness and normal efficiency with the 15% trimmed mean, but in addition it leads, like symmetric quantile averages, to the definition of exact nonparametric confidence intervals.