Concentration Ellipsoids, Their Planes of Support, and the Linear Regression Model
利用随机向量浓度椭球与其支撑平面的关系，对一般线性回归模型中的现有结果进行几何推导和解释，揭示线性无偏最小方差估计量的几何来源。
The relationship between the concentration ellipsoid of a random vector and its planes of support is exploited to provide a geometric derivation and interpretation of existing results for a general form of the linear regression model. In particular, the planes of support whose points of tangency to the ellipsoid are contained in the range (or column space) of the design matrix are the source of all linear unbiased minimum variance estimators. The connection between this idea and estimators based on projections is explored, as is also its use in obtaining and interpreting some existing relative efficiency results.