Model selection with vague prior information
- Moreno Bas, Elías
- Girón González-Torre, Francisco Javier
- Martínez García, María Lina
ISSN: 1137-2141
Year of publication: 1998
Volume: 92
Issue: 4
Pages: 289-298
Type: Article
More publications in: Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales
Abstract
In the Bayesian approach, the Bayes factor is the main tool for model selection and hypothesis testing. When prior information is weak, "default" or "automatic" priors, which are typicaIly improper, are commonly used but, unfortunately, the Bayes factor is defined up to a multiplicative constant. In this paper we revise some recent but already popular methodologies, intrinsic and lractional, to deal with improper priors in model selection and hypothesis testing. Special attention is paid to the intrinsic and fractional methods as tools devised to produce proper priors to compute actual Bayes factors. Sorne illustration to hypothesis testing problems with more than one population are given, in particular the Behrens- Fisher problem is considered.