" - a Comparison between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear
نویسنده
چکیده
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 maximum likelihood is much more sensitive to small misspecifications in the functional relationship between the error variances and the regression parameter.
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