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Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments

Forkman, Johannes and Piepho, Hans-Peter (2013). Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments. Journal of agricultural science. 151 :3 , 381-395
[Research article]

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Official URL: http://dx.doi.org/10.1017/S0021859612000445


The model for analysis of randomized complete block (RCB) experiments usually includes two factors: block and
treatment. If treatment is modelled as fixed, best linear unbiased estimation (BLUE) is used, and treatment means
estimate expected means. If treatment is modelled as random, best linear unbiased prediction (BLUP) shrinks the
treatment means towards the overall mean, which results in smaller root-mean-square error (RMSE) in prediction
of means. This theoretical result holds provided the variance components are known, but in practice the variance
components are estimated. BLUP using estimated variance components is called empirical best linear unbiased
prediction (EBLUP). In small experiments, estimates can be unreliable and the usefulness of EBLUP is uncertain.
The present paper investigates, through simulation, the performance of EBLUP in small RCB experiments with
normally as well as non-normally distributed random effects. The methods of Satterthwaite (1946) and of Kenward
& Roger (1997, 2009), as implemented in the SAS System, were studied. Performance was measured by RMSE, in
prediction of means, and coverage of prediction intervals. In addition, a Bayesian approach was used for
prediction of treatment differences and computation of credible intervals. EBLUP performed better than BLUE with
regard to RMSE, also when the number of treatments was small and when the treatment effects were non-normally
distributed. The methods of Satterthwaite and of Kenward & Roger usually produced approximately correct
coverage of prediction intervals. The Bayesian method gave the smallest RMSE and usually more accurate
coverage of intervals than the other methods.

Authors/Creators:Forkman, Johannes and Piepho, Hans-Peter
Title:Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments
Series Name/Journal:Journal of agricultural science
Year of publishing :June 2013
Page range:381-395
Publisher:Cambridge University Press
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 101 Mathematics > 10106 Probability Theory and Statistics
Keywords:Best linear unbiased prediction, Randomized complete block experiment
Permanent URL:
Additional ID:
Type of IDID
Web of Science (WoS)000322161900006
ID Code:11240
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Crop Production Ecology
Deposited By: Johannes Forkman
Deposited On:02 Jun 2014 09:46
Metadata Last Modified:24 Jan 2015 01:50

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