Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models

AR Gilmour*, R Thompson, BR Cullis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Pages (from-to)1440-1450
Number of pages11
JournalBiometrics
Volume51
Issue number4
Publication statusPublished - Dec 1995

Keywords / Materials (for Non-textual outputs)

  • EM algorithm
  • spatial analysis
  • variance components
  • REML
  • RESTRICTED MAXIMUM-LIKELIHOOD
  • GENERATION VARIETY TRIALS
  • SPARSE-MATRIX INVERSION
  • FIELD EXPERIMENTS
  • COMPONENTS

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