Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics
Statistics Theory
2022-08-11 v1 Statistics Theory
Abstract
We extend a recently established asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The new results show that the maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, random effects covariance and dispersion parameters. The dispersion parameter maximum likelihood estimator has a particularly simple asymptotic distribution which enables straightforward valid likelihood-based inference.
Cite
@article{arxiv.2208.05301,
title = {Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics},
author = {Aishwarya Bhaskaran and Matt P. Wand},
journal= {arXiv preprint arXiv:2208.05301},
year = {2022}
}