Abstract
To assess the influence of observations on the parameter estimates, case deletion diagnostics are commonly used in linear regression models. For linear models with correlated errors we study the influence of observations on testing a linear hypothesis using single and multiple case deletions. The change in likelihood ratio test and F test theoretically is derived and it is shown these tests to be completely determined by two proposed generalized externally studentized residuals. An illustrative example of a real data set is also reported.
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Acknowledgments
The authors would like to thank the Editor and an anonymous referee for several helpful comments and suggestions, which resulted in a significant improvement in the presentation of this article.
Notes
*without case 21.