Bayesian selection of multiresponse nonlinear regression model

Abstract : A Bayesian method for the selection and ranking of multiresponse nonlinear regression models in a given model set is proposed. It uses an expected utility criterion based on the logarithmic score of a posterior predictive density of each model. Two approaches are proposed to get this posterior. The first is based on a general asymptotically convergent approximation of the model parameter posterior corresponding to a wide class of parameter priors. The second, a numerical one, uses well-known pure or hybrid MCMC methods. Both posterior approximation approaches allow the practical computation of the expected utility criterion on a sample-reuse basis. This leads to a class of Bayesian cross-validation (CV) procedures, aiming at finding the model having the best predictive ability among a set of models. Varied comparisons of the performances of the Bayesian procedures, with that of AIC, BIC and standard CV procedures, are proposed on a simulated and a real model selection problem case studies, of low and high parametric dimensions respectively.
Type de document :
Article dans une revue
Statistics, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2008, 42 (4), pp.291-311. 〈10.1080/02331880701739824〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00857812
Contributeur : Alain Rapaport <>
Soumis le : mercredi 4 septembre 2013 - 10:02:52
Dernière modification le : jeudi 20 juillet 2017 - 16:33:39

Lien texte intégral

Identifiants

Collections

Citation

Vivien Rossi, Jean-Pierre Vila. Bayesian selection of multiresponse nonlinear regression model. Statistics, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2008, 42 (4), pp.291-311. 〈10.1080/02331880701739824〉. 〈hal-00857812〉

Partager

Métriques

Consultations de la notice

84