• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail - No thumbnail

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 publié
External document link
https://arxiv.org/abs/1306.6928v1
Date
2015
Journal name
Statistica Sinica
Volume
25
Number
3
Publisher
Institute of Statistical Science
Published in
Paris
Pages
1009-1024
Publication identifier
10.5705/ss.2013.338
Metadata
Show full item record
Author(s)
Salmeron, Diego

Cano, Juan Antonio

Robert, Christian P.
Abstract (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 mod
JEL
C11 - Bayesian Analysis: General

Related items

Showing items related by title and author.

  • Thumbnail
    Integral equation solutions as prior distributions for Bayesian model selection 
    Cano, Juan Antonio; Salmeron, Diego; Robert, Christian P. (2008) Article accepté pour publication ou publié
  • Thumbnail
    Bayesian model comparison in cosmology with Population Monte Carlo 
    Kilbinger, Martin; Wraith, Darren; Robert, Christian P.; Benabed, Karim; Cappé, Olivier; Cardoso, Jean-François; Fort, Gersende; Prunet, Simon; Bouchet, François R. (2010) Article accepté pour publication ou publié
  • Thumbnail
    Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation 
    Celeux, Gilles; El Anbari, Mohammed; Marin, Jean-Michel; Robert, Christian P. (2012) Article accepté pour publication ou publié
  • Thumbnail
    Bayesian computation for statistical models with intractable normalizing constants 
    Lartillot, Nicolas; Robert, Christian P.; Atchade, Yves (2012) Article accepté pour publication ou publié
  • Thumbnail
    Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics. 
    Cornuet, Jean-Marie; Robert, Christian P.; Pudlo, Pierre; Guillemaud, Thomas; Marin, Jean-Michel; Lombaert, Eric; Estoup, Arnaud (2012) Article accepté pour publication ou publié
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo