Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions

TL;DR

This paper develops an objective Bayesian approach using integral priors for model selection in binomial regression models, facilitating epidemiological risk factor analysis without the need for tuning parameters.

Contribution

It introduces a nearly automatic method for constructing reference priors in binomial regression, enabling Bayesian model selection with integral priors and objective Bayes factors.

Findings

Effective Bayesian model selection in binomial regression models.

Automatic construction of reference priors without tuning.

Application to epidemiological risk factor analysis.

Abstract

In this work we apply the methodology of integral priors to handle Bayesian model selection in binomial regression models with a general link function. These models are very often used to investigate associations and risks in epidemiological studies where one goal is to exhibit whether or not an exposure is a risk factor for developing a certain disease; the purpose of the current paper is to test the effect of specific exposure factors. We formulate the problem as a Bayesian model selection case and solve it using objective Bayes factors. To construct the reference prior distributions on the regression coefficients of the binomial regression models, we rely on the methodology of integral priors that is nearly automatic as it only requires the specification of estimation reference priors and it does not depend on tuning parameters or on hyperparameters within these priors.