Bayesian inference for generalized linear models via quasi-posteriors.

ABSTRACT: Generalized linear models are routinely used for modelling relationships between a response variable and a set of covariates. The simple form of a generalized linear model comes with easy interpretability, but also leads to concerns about model misspecification impacting inferential conclusions. A popular semiparametric solution adopted in the frequentist literature is quasilikelihood, which improves robustness by only requiring correct specification of the first two moments. We develop a robust approach to Bayesian inference in generalized linear models through quasi-posterior distributions. We show that quasi-posteriors provide a coherent generalized Bayes inference method, while also approximating so-called coarsened posteriors. In so doing, we obtain new insights into the choice of coa

SUBMITTER: Agnoletto D 

PROVIDER: S-EPMC12206450 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature