Regression Diagnostics for General Linear Regression Models
研究了线性回归模型中，当误差项具有一般结构时，估计量对误差协方差矩阵变化的局部敏感性，并给出了诊断方法，适用于自相关误差等复杂情形。
Abstract For the linear regression model y = Xβ + ε the local sensitivities of estimates for β are investigated with respect to a general error structure for the residuals ε. In particular, we define local sensitivity analysis as matrix derivatives of the general least squares (GLS) estimator with respect to changes in the weight matrix ∑-1, where ∑ = var(ε). The results are extensions of derivative formulas given in Belsley, Kuh, and Welsch (1980) for ordinary least squares (OLS) regressions. An example is given showing how the new derivatives can be used for regression diagnostics in regression models with autocorrelated errors.