Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions
Salmeron, Diego; Cano, Juan Antonio; Robert, Christian P. (2015), Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions, Statistica Sinica, 25, 3, p. 1009-1024. 10.5705/ss.2013.338
Type
Article accepté pour publication ou publiéExternal document link
https://arxiv.org/abs/1306.6928v1Date
2015Journal name
Statistica SinicaVolume
25Number
3Publisher
Institute of Statistical Science
Published in
Paris
Pages
1009-1024
Publication identifier
Metadata
Show full item recordAbstract (EN)
In this work we apply the methodology of integral priors to deal with Bayesian model selection in nested binomial regression models with a general link function. These models are often used to investigate associations and risks in epidemiological studies where one goal is to find 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 one and solve it using objective Bayes factors. To elicit 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.Subjects / Keywords
Markov chain; Integral prior; Jeffreys prior; Objective Bayes factor; Binomial regression modRelated items
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