AIMS: Average information matrix splitting

Shengxin Zhu, Tongxiang Gu, Xingping Liu

Abstract: 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.