Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances
针对非球形扰动的线性回归模型，利用Stein规则估计量改进了回归系数向量的置信集，并推导了大样本近似下的覆盖概率和期望体积，比较了可行广义最小二乘估计量与Stein规则估计量的优劣。
In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.