Analysis of Linear Combinations With Extreme Ratios of Variance
当两个多元总体的协方差矩阵不同时，通过分析方差极端比值的线性组合来揭示差异，并给出类似回归中变量选择的方法来简化这些组合，同时定义了变量冗余假设并推导了检验统计量。
Abstract If the covariance matrices Σ1 and Σ2 of two multivariate populations are not identical, insight into the differences between Σ1 and Σ2 can often be gained by analyzing the linear combinations with extreme ratios of variances—that is, those defined by the eigenvectors associated with the extreme roots of Σ1 −1 Σ2. This article gives a descriptive method, similar to variable selection procedures in regression, for screening and simplifying these linear combinations. A hypothesis of redundancy of variables is defined, and a statistic for testing this hypothesis is derived. The method is illustrated by an example.