Bayesian selection of multiresponse nonlinear regression model

Rossi Vivien, Vila Jean-Pierre. 2008. Bayesian selection of multiresponse nonlinear regression model. Statistics, 42 (4) : 291-311.

Résumé : 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.

Mots-clés Agrovoc : modèle mathématique, modèle de simulation

Classification Agris : U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : Hors axes (2005-2013)

Auteurs et affiliations

• Rossi Vivien, CIRAD-ES-UPR Dynamique forestière (FRA) ORCID: 0000-0001-5458-1523
• Vila Jean-Pierre, INRA (FRA)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/545790/)

Métriques

[ Page générée et mise en cache le 2024-11-21 ]