Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models
Abstract
The restricted maximum likelihood method enhances popularity of maximum likelihood methods for variance component analysis on large scale unbalanced data. As the high throughput biological data sets and the emerged science on uncertainty quantification, such a method receives increasing attention. Estimating the unknown variance parameters with restricted maximum likelihood method usually requires an nonlinear iterative method. Therefore proper formulae for the log-likelihood function and its derivatives play an essential role in practical algorithm design. It is our aim to provide a mathematical introduction to this method, and supply a self-contained derivation on some available formulae used in practical algorithms. Some new proof are supplied.
Keywords
Cite
@article{arxiv.1805.05188,
title = {Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models},
author = {Shengxin Zhu and Andrew J Wathen},
journal= {arXiv preprint arXiv:1805.05188},
year = {2018}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1608.07207