A Loss-Based Prior for Variable Selection in Linear Regression Methods

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dc.contributor.author Villa, C en
dc.contributor.author Lee, Jeong en
dc.date.accessioned 2019-10-02T00:39:49Z en
dc.date.issued 2019 en
dc.identifier.citation Bayesian Analysis 26 pages 2019 en
dc.identifier.issn 1936-0975 en
dc.identifier.uri http://hdl.handle.net/2292/48329 en
dc.description.abstract In this work we propose a novel model prior for variable selection in linear regression. The idea is to determine the prior mass by considering the worth of each of the regression models, given the number of possible covariates under consideration. The worth of a model consists of the information loss and the loss due to model complexity. While the information loss is determined objectively, the loss expression due to model complexity is flexible and, the penalty on model size can be even customized to include some prior knowledge. Some versions of the loss-based prior are proposed and compared empirically. Through simulation studies and real data analyses, we compare the proposed prior to the Scott and Berger prior, for noninformative scenarios, and with the Beta-Binomial prior, for informative scenarios. en
dc.publisher International Society for Bayesian Analysis en
dc.relation.ispartofseries Bayesian Analysis en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://projecteuclid.org/policy/euclid.ba en
dc.title A Loss-Based Prior for Variable Selection in Linear Regression Methods en
dc.type Journal Article en
dc.identifier.doi 10.1214/19-BA1162 en
dc.rights.holder Copyright: The authors en
pubs.author-url https://projecteuclid.org/euclid.ba/1560477728 en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Article en
pubs.elements-id 775361 en
pubs.org-id Science en
pubs.org-id Statistics en
dc.identifier.eissn 1931-6690 en
pubs.record-created-at-source-date 2019-06-25 en
pubs.online-publication-date 2019-06-24 en

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