Abstract
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.
Keywords
HeteroscedasticityMathematicsStatisticsGeneralized least squaresGeneralized linear modelRestricted maximum likelihoodLinear regressionMaximum likelihoodTotal least squaresRegression analysisLeast-squares function approximationLinear modelLikelihood-ratio testMaximum likelihood sequence estimationEconometrics
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Publication Info
- Year
- 1982
- Type
- article
- Volume
- 77
- Issue
- 380
- Pages
- 878-882
- Citations
- 113
- Access
- Closed
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Cite This
Raymond J. Carroll,
David Ruppert
(1982).
A Comparison between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear Model.
Journal of the American Statistical Association
, 77
(380)
, 878-882.
https://doi.org/10.1080/01621459.1982.10477901
Identifiers
- DOI
- 10.1080/01621459.1982.10477901