Titre :

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

Auteur(s) :

Nguyen, Thi Le Thu [Auteur]
LAGIS-SI
Septier, Francois [Auteur]
LAGIS-SI
Peters, Gareth W. [Auteur]
University College of London [London] [UCL]
Delignon, Yves [Auteur]
LAGIS-SI

Titre de la manifestation scientifique :

21st European Signal Processing Conference (EUSIPCO)

Ville :

Marrakech

Pays :

Maroc

Date de début de la manifestation scientifique :

2013-09-09

Titre de l’ouvrage :

21st European Signal Processing Conference (EUSIPCO)

Date de publication :

2013-09-09

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL