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- OpenAccess
A Diagnostic for Assessing the Influence of Cases on the Prediction of Random Effects in a Mixed Model
並列摘要
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.
延伸閱讀
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