Title :

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

Author(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

Conference title :

21st European Signal Processing Conference (EUSIPCO)

City :

Marrakech

Country :

Maroc

Start date of the conference :

2013-09-09

Book title :

21st European Signal Processing Conference (EUSIPCO)

Publication date :

2013-09-09

HAL domain(s) :

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]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL