A Comparison between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear Model
异方差线性模型中最大似然与广义最小二乘的比较
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.