Deletion diagnostics for generalised estimating equations

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Abstract
SUMMARYDeletion diagnostics are proposed for generalised estimating equations. The diagnostics consider leverage and residuals to measure the influence of a subset of observations on the estimated regression parameters and on the estimated values of the linear predictor. Computational formulae are provided which correspond to the influence of a single observation and of an entire cluster of correlated observations. Additionally, diagnostics are given which approximate the effect of deletion of an arbitrary subset of observations under a model with general covariance structure and arbitrary link function, extending Proposition 3 of Christensen, Pearson & Johnson (1992). The proposed measures are applied to medical data.

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