Objective bayesian Hypothesis Testing in Binomial Regression Models with Integral Prior Distributions

Diego Salmeron, Juan Antonio Cano and Christian Robert (xian@ceremade.dauphine.fr)
Additional contact information
Diego Salmeron: CIBERESP
Juan Antonio Cano: Universidad de Murcia
Christian Robert: Université Paris-Dauphine et CREST

No 2013-44, Working Papers from Center for Research in Economics and Statistics

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

Keywords: Binomial regression model; Integral prior; Jeffreys prior; Markov chain; Objective Bayes factor (search for similar items in EconPapers)
Pages: 17
Date: 2013-12
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