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September 2013 Some Priors for Sparse Regression Modelling
Jim E. Griffin, Philip. J. Brown
Bayesian Anal. 8(3): 691-702 (September 2013). DOI: 10.1214/13-BA827

Abstract

A wide range of methods, Bayesian and others, tackle regression when there are many variables. In the Bayesian context, the prior is constructed to reflect ideas of variable selection and to encourage appropriate shrinkage. The prior needs to be reasonably robust to different signal to noise structures. Two simple evergreen prior constructions stem from ridge regression on the one hand and g-priors on the other. We seek to embed recent ideas about sparsity of the regression coefficients and robustness into these priors. We also explore the gains that can be expected from these differing approaches.

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Jim E. Griffin. Philip. J. Brown. "Some Priors for Sparse Regression Modelling." Bayesian Anal. 8 (3) 691 - 702, September 2013. https://doi.org/10.1214/13-BA827

Information

Published: September 2013
First available in Project Euclid: 9 September 2013

zbMATH: 1329.62132
MathSciNet: MR3102230
Digital Object Identifier: 10.1214/13-BA827

Keywords: Canonical reduction , Correlated priors , g-priors , Markov chain Monte Carlo , multiple regression , normal-gamma prior , p>n , Ridge regression , robust priors , Sparsity

Rights: Copyright © 2013 International Society for Bayesian Analysis

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Vol.8 • No. 3 • September 2013
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