A Comparison between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear Model Academic Article uri icon

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. 1982 Taylor & Francis Group, LLC.

published proceedings

  • Journal of the American Statistical Association

author list (cited authors)

  • Carroll, R. J., & Ruppert, D.

citation count

  • 76

complete list of authors

  • Carroll, RJ||Ruppert, David

publication date

  • December 1982