Pré-Publication, Document De Travail Année : 2011

Ridge parameter for g-prior distribution in Probit mixed model

Résumé

In the Bayesian variable selection framework, a common prior distribution for the regression coefficients is the g-prior of Zellner (1986). However, there are two standard cases in which the associated covariance matrix does not exist, and the conventional prior of Zellner can not be used: if the number of observations is lower than the number of variables (large p and small n paradigm), or if some variables are linear combinations of others. In such situations we propose a prior distribution derived from the prior of Zellner, by introducing a ridge parameter. The prior obtained is a flexible and simple adaptation of the g-prior. This adaptation is a compromise between the conditional independent case of the coefficient regressors and the automatic scaling advantage offered by the g-prior. A full variable selection method using this prior is developed for probit mixed models, with a Metropolis-within-Gibbs algorithm using the grouping technique of Liu (1994). The method is then applied to both simulated and real datasets.

Fichier principal
Vignette du fichier
selection_variable_ridge-HAL1.pdf (215.23 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-00561824 , version 1 (02-02-2011)
hal-00561824 , version 2 (04-03-2011)
hal-00561824 , version 3 (30-05-2011)

Licence

Identifiants

Citer

Meili Baragatti, Denys Pommeret. Ridge parameter for g-prior distribution in Probit mixed model. 2011. ⟨hal-00561824v1⟩
66 Consultations
404 Téléchargements

Altmetric

Partager

  • More