AIMS:Average Information Matrix Splitting

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.

References in corpus (3)

• Generalised Fisher Matrices
• Computing log-likelihood and its derivatives for restricted maximum likelihood methods
• Information Splitting for Big Data Analytics

Cited by in corpus (3)

• Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models
• Computing log-likelihood and its derivatives for restricted maximum likelihood methods
• Information Splitting for Big Data Analytics