Generalized prediction intervals for treatment effects in random‐effects models

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 s...

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Bibliographic Details
Published inBiometrical journal Vol. 61; no. 5; pp. 1242 - 1257
Main Authors Al‐Sarraj, Razaw, von Brömssen, Claudia, Forkman, Johannes
Format Journal Article
LanguageEnglish
Published Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.09.2019
Subjects
Agricultural land
Agriculture
Approximation
Avena - growth & development
Biometry - methods
Computer simulation
generalized prediction intervals
Intervals
Models, Statistical
Probability Theory and Statistics
random effects
random models
Regression analysis
REML
Sannolikhetsteori och statistik
Simulation
generalized prediction intervals
random effects
random models
REML
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.201700255