On the Estimation of Slope and the Identification of Outliers in Linear Regression
提出一种基于误差分布经验累积生成函数的大样本方法，用于估计线性模型中的斜率参数，并利用正态分布特征识别最小二乘下的异常值。
The paper presents a large sample method for estimating the slope parameter in a linear model by minimizing a loss function related to the empirical cumulant generating function of the error distribution. A family of estimators, indexed by a real parameter, is obtained and consistency and asymptotic normality established. The optimum member of the family is that which has minimum variance with respect to the parameter. This minimization together with a characterization result for the normal distribution leads to a procedure for the identification of outliers with respect to least squares.