Aims: Average information matrix splitting

Shengxin Zhu*, Tongxiang Gu, Xingping Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


For linear mixed models with co-variance matrices which are not linearly dependent on variance component parameters, we prove that the average of the observed information and the Fisher information can be split into two parts. The essential part enjoys a simple and computational friendly formula, while the other part which involves a lot of computations is a random zero matrix and thus is negligible.

Original languageEnglish
Pages (from-to)301-308
Number of pages8
JournalMathematical Foundations of Computing
Issue number4
Publication statusPublished - Nov 2020


  • Fisher information matrix
  • Newton method
  • Observed information matrix
  • average information
  • linear mixed model
  • variance parameter estimation


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