A Comparison Between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear Model
研究了误差方差与回归参数存在函数关系时，最大似然估计和广义最小二乘估计对函数关系微小误设的敏感程度，发现最大似然更敏感。
Abstract We consider a linear model with normally distributed but heteroscedastic errors. When the error variances are functionally related to the regression parameter, one can use either maximum likelihood or generalized least squares to estimate the regression parameter. We show that likelihood is more sensitive to small misspecifications in the functional relationship between the error variances and the regression parameter.