AIMS: Average information matrix splitting
Shengxin Zhu, Tong-Xiang Gu, Xingping Liu · Mathematical Foundations of Computing · 2020
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.