On Confidence Regions in Canonical Variate Analysis
指出传统典型变量图中的置信圆不正确，因未考虑轴抽样变异性；通过近似推导正确区域，自助法验证其概率精度远优于传统圆，并用实例展示差异。
It is argued that confidence circles as traditionally drawn on canonical variate diagrams are incorrect, because no allowance is made in their construction for the sampling variability of the canonical variate axes. A series of approximations is employed to obtain tractable expressions for the correct regions. Despite the rather broad approximations involved, empirical assessment via bootstrapping shows the new regions to have much more accurate probability content than the traditional circles. An illustrative example highlights the differences between the two types of region.