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Prediction in linear mixed models

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Welham, S., Cullis, B. R., Gogel, B., Gilmour, A. and Thompson, R. (2004) Prediction in linear mixed models. Australian and New Zealand Journal of Statistics, 46 (3). pp. 325-347. ISSN 1467-842X

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Article Link: https://doi.org/10.1111/j.1467-842X.2004.00334.x


Following estimation of effects from a linear mixed model, it is often useful to form predicted values for certain factor/variate combinations. The process has been well defined for linear models, but the introduction of random effects into the model means that a decision has to be made about the inclusion or exclusion of random model terms from the predictions. This paper discusses the interpretation of predictions formed including or excluding random terms. Four datasets are used to illustrate circumstances where different prediction strategies may be appropriate: in an orthogonal design, an unbalanced nested structure, a model with cubic smoothing spline terms and for kriging after spatial analysis. The examples also show the need for different weighting schemes that recognize nesting and aliasing during prediction, and the necessity of being able to detect inestimable predictions.

Item Type:Article
Subjects:Science > Statistics > Statistical data analysis
Science > Statistics > Simulation modelling
Live Archive:31 Jan 2024 01:45
Last Modified:31 Jan 2024 01:45

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