Abstract
Although the linear mixed model can be viewed as a direct extension of multiple regression, it is not obvious how to generalize the standard diagnostic tools such as residual analysis and detection of leverage points and outliers, which are available in the linear regression situation. A unified approach to residuals, leverages and outliers in the linear mixed model is developed. Formal and informal procedures are proposed to display the general features of residuals and leverages in order to detect outliers and high-leverage points in the linear mixed models. The relationship between the best linear unbiased predictor (BLUP) and residuals is established. Some properties of BLUPs are formulated and their use in detecting outlying observations are investigated.
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