Linear models for uncorrelated data have well established measures to gauge the influence of one or more observations on the analysis. For such models, closed-form update expressions allow efficient computations without refitting the model. When similar notions of statistical influence are applied to mixed models, things are more complicated. Removing data points affects fixed effects and covariance parameter estimates. Update formulas for "Leave-one-out" estimates typically fail to account for changes in covariance parameters. Moreover, in repeated measures or longitudinal studies, one is often interested in multivariate influence, rather than the impact of isolated points. This paper examines extensions of influence measures in linear mixed models and their implementation in the MIXED procedure.
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