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.
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https://hal-imt.archives-ouvertes.fr/hal-00836918
Contributor : François Septier <>
Submitted on : Friday, June 21, 2013 - 5:10:14 PM
Last modification on : Thursday, February 21, 2019 - 10:34:10 AM

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  • HAL Id : hal-00836918, version 1

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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. ⟨hal-00836918⟩

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