Some Priors for Sparse Regression Modelling

Griffin, Jim E. and Brown, Philip J. (2013) Some Priors for Sparse Regression Modelling. Bayesian Analysis, 8 (3). pp. 691-702. ISSN 1936-0975. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://ba.stat.cmu.edu/journal/2013/vol08/issue03/...

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

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 29 May 2014 15:36 UTC
Last Modified: 29 May 2014 15:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41221 (The current URI for this page, for reference purposes)
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