Bayesian Model Selection and Parameter Estimation in Penalized Regression Model Using SMC Samplers

Abstract : Penalized regression methods have received a great deal of attention in recent years, mostly through frequentist models using l1-regularization. However, all existing works assume that the design matrix, that links the explanatory variables to the observed response, is known a priori. Unfortunately, this is often not the case and thus solving this challenging problem is of considerable interest. In this paper, we look at a fully Bayesian formulation of this problem. This paper proposes the use of Sequential Monte Carlo samplers for joint model selection and parameter estimation. Furthermore, a new class of priors based on α-stable family distribution is proposed as non-convex penalty for regularization of the regression coef- ficients. The performance of the proposed methodology is demonstrated in two different settings.
Type de document :
Communication dans un congrès
21st European Signal Processing Conference (EUSIPCO), Sep 2013, Marrakech, Morocco. pp.1-5, 2013
Liste complète des métadonnées

https://hal-imt.archives-ouvertes.fr/hal-00836918
Contributeur : François Septier <>
Soumis le : vendredi 21 juin 2013 - 17:10:14
Dernière modification le : jeudi 11 janvier 2018 - 06:26:40

Identifiants

  • HAL Id : hal-00836918, version 1

Collections

Citation

Thi Le Thu Nguyen, François Septier, Gareth W. Peters, Yves Delignon. Bayesian Model Selection and Parameter Estimation in Penalized Regression Model Using SMC Samplers. 21st European Signal Processing Conference (EUSIPCO), Sep 2013, Marrakech, Morocco. pp.1-5, 2013. 〈hal-00836918〉

Partager

Métriques

Consultations de la notice

169