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Generalized prediction intervals for treatment effects in random-effects models
Razaw Al-Sarraj1, Claudia von Brömssen1, Johannes Forkman1,2
1Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Abstract:
This article derives generalized prediction intervals for random effects in linear random-effects models. For balanced and unbalanced data in two-way layouts, models are considered with and without interaction. Coverage of the proposed generalized prediction intervals was estimated in a simulation study based on an agricultural field experiment. Generalized prediction intervals were compared with prediction intervals based on the restricted maximum likelihood (REML) procedure and the approximate methods of Satterthwaite and Kenward and Roger. The simulation study showed that coverage of generalized prediction intervals was closer to the nominal level 0.95 than coverage of prediction intervals based on the REML procedure.
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