Generalized prediction intervals for treatment effects in random-effects models

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.

Keywords

generalized prediction intervals; random effects; random models; REML

Published in

Biometrical Journal
2019, volume: 61, number: 5, pages: 1242-1257
Publisher: WILEY

SLU Authors

• Al-Sarraj, Razaw

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

• von Brömssen, Claudia

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

• Forkman, Johannes

  • Department of Crop Production Ecology, Swedish University of Agricultural Sciences
    
  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1002/bimj.201700255

Permanent link to this page (URI)

https://res.slu.se/id/publ/101959