Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19.

ABSTRACT: In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual P

SUBMITTER: Murakami D 

PROVIDER: S-EPMC8741021 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature