Representations of the Space of Distributions Useful in Robust Estimation of Location
本文给出了分布空间的一维、二维和三维表示，这些表示基于秩估计量的渐近相对效率定义距离，并通过多维尺度分析得到，有助于研究位置参数的稳健估计。
In many situations it is useful to have a low-dimensional representation of the space of distributions. This paper gives one-, two- and three-dimensional representations which are particularly relevant to the study of robust estimation of location based on rank estimators. The distances are defined as functions of the asymptotic relative efficiency of the most efficient rank estimator for one distribution when used on data from another distribution. Values of these distance functions are computed for a large number of pairs of distributions and multidimensional scaling is used to find the low-dimensional representations.