Bayesian model selection and parameter estimation in penalized regression model using SMC samplers

Thi Le Thu Nguyen, François Septier, Gareth W. Peters, Yves Delignon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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 coefficients. The performance of the proposed methodology is demonstrated in two different settings.

Original languageEnglish
Title of host publication21st European Signal Processing Conference (EUSIPCO 2013)
PublisherIEEE
ISBN (Print)9780992862602
Publication statusPublished - 8 May 2014
Event21st European Signal Processing Conference 2013 - Morocco, Marrakech, Morocco
Duration: 9 Sep 201313 Sep 2013

Conference

Conference21st European Signal Processing Conference 2013
Abbreviated titleEUSIPCO 2013
CountryMorocco
CityMarrakech
Period9/09/1313/09/13

Keywords

  • Bayesian Inference
  • Model Selection
  • Regularization
  • SMC sampler

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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