Gaussian variational approximation with composite likelihood for crossed random effect models
Statistics Theory
2023-10-23 v2 Statistics Theory
Abstract
Composite likelihood usually ignores dependencies among response components, while variational approximation to likelihood ignores dependencies among parameter components. We derive a Gaussian variational approximation to the composite log-likelihood function for Poisson and Gamma regression models with crossed random effects. We show consistency and asymptotic normality of the estimates derived from this approximation and support this theory with some simulation studies. The approach is computationally much faster than a Gaussian variational approximation to the full log-likelihood function.
Cite
@article{arxiv.2310.12485,
title = {Gaussian variational approximation with composite likelihood for crossed random effect models},
author = {Libai Xu and Nancy Reid and Dehan Kong},
journal= {arXiv preprint arXiv:2310.12485},
year = {2023}
}