Efficient sampling for Gaussian linear regression with arbitrary priors
| dc.contributor.author | Hahn, P. Richard | |
| dc.contributor.author | He, Jingyu | |
| dc.contributor.author | HEDIBERT FREITAS LOPES | |
| dc.coverage.cidade | Não informado | pt_BR |
| dc.coverage.pais | Não Informado | pt_BR |
| dc.creator | Hahn, P. Richard | |
| dc.creator | He, Jingyu | |
| dc.date.accessioned | 2022-08-23T20:02:56Z | |
| dc.date.available | 2022-08-23T20:02:56Z | |
| dc.date.issued | 2018 | |
| dc.description.other | This article develops a slice sampler for Bayesian linear regression models with arbitrary priors. The new sampler has two advantages over current approaches. One, it is faster than many custom implementations that rely on auxiliary latent variables, if the number of regressors is large. Two, it can be used with any prior with a density function that can be evaluated up to a normalizing constant, making it ideal for investigating the properties of new shrinkage priors without having to develop custom sampling algorithms. The new sampler takes advantage of the special structure of the linear regression likelihood, allowing it to produce better effective sample size per second than common alternative approaches. | pt_BR |
| dc.format.extent | p. 142-154 | pt_BR |
| dc.format.medium | Digital | pt_BR |
| dc.identifier.doi | 10.1080/10618600.2018.1482762 | pt_BR |
| dc.identifier.issn | 15372715 | pt_BR |
| dc.identifier.issue | 1 | pt_BR |
| dc.identifier.uri | https://repositorio.insper.edu.br/handle/11224/4099 | |
| dc.identifier.volume | 28 | pt_BR |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | Não informado | pt_BR |
| dc.relation.ispartof | Journal of Computational and Graphical Statistics | pt_BR |
| dc.rights.license | O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR. | pt_BR |
| dc.subject.keywords | Bayesian computation | pt_BR |
| dc.subject.keywords | Linear regression | pt_BR |
| dc.subject.keywords | Shrinkage priors | pt_BR |
| dc.subject.keywords | Slice sampling | pt_BR |
| dc.title | Efficient sampling for Gaussian linear regression with arbitrary priors | pt_BR |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| local.identifier.sourceUri | https://www.tandfonline.com/doi/full/10.1080/10618600.2018.1482762 | |
| local.subject.cnpq | Ciências Sociais Aplicadas | pt_BR |
| local.type | Artigo Científico | pt_BR |
| relation.isAuthorOfPublication | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca | |
| relation.isAuthorOfPublication.latestForDiscovery | 41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca |
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