Influence Diagnostics on Testing Linear Hypothesis in Linear Models with Correlated Errors

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.

Keywords:

• Case deletion
• Influence diagnostics
• Likelihood ratio test
• Mean-shift outlier

Mathematics Subject Classification:

• Primary 62J05
• Secondary 62J20

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.