The Mahalanobis Distance and Elliptic Distributions
证明了马氏距离是衡量位置不同但形状相同的两个椭圆分布之间距离的合适度量，将多元分析中一个熟知结果推广到非正态分布类。
The Mahalanobis distance is shown to be an appropriate measure of distance between two elliptic distributions having different locations but a common shape. This extends a result long familiar in multivariate analysis to a class of nonnormal distributions. It can also be used to show that the sample version of the Mahalanobis distance is appropriate under both estimative and predictive approaches to estimation for the family of multivariate normal distributions differing only in location.