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 |
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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 |
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dc.rights.uri |
https://projecteuclid.org/policy/euclid.ba |
en |
dc.title |
A Loss-Based Prior for Variable Selection in Linear Regression Methods |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.1214/19-BA1162 |
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dc.rights.holder |
Copyright: The authors |
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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 |