Benchmark priors for Bayesian model averaging
In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpect...
| Autores: | , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2000 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/341960 |
| Acesso em linha: | http://hdl.handle.net/10261/341960 https://api.elsevier.com/content/abstract/scopus_id/18044404766 |
| Access Level: | acceso abierto |
| Palavra-chave: | Bayes factors Markov chain Monte Carlo Posterior odds Prior elicitation |
| id |
ES_0b5aa2ed7e832c0ac97cbfea5a3c7003 |
|---|---|
| oai_identifier_str |
oai:digital.csic.es:10261/341960 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
Benchmark priors for Bayesian model averagingFernández-Llana, CarmenLey, EduardoSteel, Mark F.J.Bayes factorsMarkov chain Monte CarloPosterior oddsPrior elicitationIn contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an 'automatic' or 'benchmark' prior structure that can be used in such cases. We focus on the normal linear regression model with uncertainty in the choice of regressors. We propose a partly non-informative prior structure related to a natural conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g0j. The consequences of different choices for g0j are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. More importantly, we examine the finite sample implications of several choices of g0j in a simulation study. The use of the MC3 algorithm of Madigan and York (Int. Stat. Rev. 63 (1995) 215), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a 'benchmark' prior specification in a linear regression context with model uncertainty. © 2001 Elsevier Science S.A. All rights reserved.Carmen Fernández gratefully acknowledges financial support from a Training and Mobility of Researchers grant awarded by the European Commission (ERBFMBICT # 961021). Carmen Fernández and Mark Steel were affiliated to CentER and the Department of Econometrics, Tilburg University, The Netherlands, and Eduardo Ley was at FEDEA, Madrid, Spain during the early stages of the work on this paper. Some of this research was done when Carmen Fernández was at the Department of Mathematics, University of Bristol, and Mark Steel at the Department of Economics, University of Edinburgh.Peer reviewedElsevierEuropean CommissionTilburg UniversityUniversity of Bristol202420242000info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/341960https://api.elsevier.com/content/abstract/scopus_id/18044404766reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésJournal of Econometricshttps://doi.org/10.1016/S0304-4076(00)00076-2Noinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3419602026-05-22T06:33:51Z |
| dc.title.none.fl_str_mv |
Benchmark priors for Bayesian model averaging |
| title |
Benchmark priors for Bayesian model averaging |
| spellingShingle |
Benchmark priors for Bayesian model averaging Fernández-Llana, Carmen Bayes factors Markov chain Monte Carlo Posterior odds Prior elicitation |
| title_short |
Benchmark priors for Bayesian model averaging |
| title_full |
Benchmark priors for Bayesian model averaging |
| title_fullStr |
Benchmark priors for Bayesian model averaging |
| title_full_unstemmed |
Benchmark priors for Bayesian model averaging |
| title_sort |
Benchmark priors for Bayesian model averaging |
| dc.creator.none.fl_str_mv |
Fernández-Llana, Carmen Ley, Eduardo Steel, Mark F.J. |
| author |
Fernández-Llana, Carmen |
| author_facet |
Fernández-Llana, Carmen Ley, Eduardo Steel, Mark F.J. |
| author_role |
author |
| author2 |
Ley, Eduardo Steel, Mark F.J. |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
European Commission Tilburg University University of Bristol |
| dc.subject.none.fl_str_mv |
Bayes factors Markov chain Monte Carlo Posterior odds Prior elicitation |
| topic |
Bayes factors Markov chain Monte Carlo Posterior odds Prior elicitation |
| description |
In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an 'automatic' or 'benchmark' prior structure that can be used in such cases. We focus on the normal linear regression model with uncertainty in the choice of regressors. We propose a partly non-informative prior structure related to a natural conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g0j. The consequences of different choices for g0j are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. More importantly, we examine the finite sample implications of several choices of g0j in a simulation study. The use of the MC3 algorithm of Madigan and York (Int. Stat. Rev. 63 (1995) 215), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a 'benchmark' prior specification in a linear regression context with model uncertainty. © 2001 Elsevier Science S.A. All rights reserved. |
| publishDate |
2000 |
| dc.date.none.fl_str_mv |
2000 2024 2024 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Postprint info:eu-repo/semantics/acceptedVersion |
| format |
article |
| status_str |
acceptedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10261/341960 https://api.elsevier.com/content/abstract/scopus_id/18044404766 |
| url |
http://hdl.handle.net/10261/341960 https://api.elsevier.com/content/abstract/scopus_id/18044404766 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
Journal of Econometrics https://doi.org/10.1016/S0304-4076(00)00076-2 No |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Elsevier |
| publisher.none.fl_str_mv |
Elsevier |
| dc.source.none.fl_str_mv |
reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
| instname_str |
Consejo Superior de Investigaciones Científicas (CSIC) |
| reponame_str |
DIGITAL.CSIC. Repositorio Institucional del CSIC |
| collection |
DIGITAL.CSIC. Repositorio Institucional del CSIC |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869403216085516288 |
| score |
15.812429 |