A Diagnostic for Assessing the Influence of Cases on the Prediction of Random Effects in a Mixed Model

by Joseph E. Cavanaugh and Junfeng Shang

Journal of Data Science, v.3, no.2, 137-151

Abstract

A diagnostic defined in terms of the Kullback-Leibler directed divergence is developed for identifying cases which impact the prediction of the random effects in a mixed model. The diagnostic compares two conditional densities governing the prediction of the random effects: one based on parameter estimates computed using the full data set, the other based on parameter estimates computed using a case-deleted data set. We present the definition of the diagnostic and derive a formula for its evaluation. Its performance is investigated in an application where exam scores are modeled using a mixed model containing a fixed exam effect and a random subject effect.

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