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Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models
Arthur R. Gilmour, Robin Thompson and Brian R. Cullis
Vol. 51, No. 4 (Dec., 1995), pp. 1440-1450
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2533274
Page Count: 11
You can always find the topics here!Topics: Algorithms, Statistical variance, Biometrics, Maximum likelihood estimation, Genotypes, Experimentation, Slates, Field experiments, Covariance, Matrices
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A strategy of using an average information matrix is shown to be computationally convenient and efficient for estimating variance components by restricted maximum likelihood (REML) in the mixed linear model. Three applications are described. The motivation for the algorithm was the estimation of variance components in the analysis of wheat variety means from 1,071 experiments representing 10 years and 60 locations in New South Wales. We also apply the algorithm to the analysis of designed experiments by incomplete block analysis and spatial analysis of field experiments.
Biometrics © 1995 International Biometric Society