A Simple Derivation of Deletion Diagnostic Results for the General Linear Model with Correlated Errors

Summary A general, simple and intuitive derivation is provided for diagnostics associated with the deletion of arbitrary subsets for the linear model with general covariance structure. These are seen to be most simply expressed, even for the well-studied case of independent and identically distributed data, in terms of a residual known variously as the conditional residual, the deletion prediction residual and the cross-validation residual. Particularly simple specializations arise when the subsets are of size 1 and of size 2, but the method is easy to apply for all subsets and to conditional deletions.