Gaussian variational approximation with composite likelihood for crossed random effect models

2023/10/19 by Libai Xu, Nancy Reid, Xu, Libai +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Mathematics #Genetic and phenotypic traits in livestock #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2310.12485

openalex publication_date 2023/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects

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