Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/9637
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lamnisos, Demetris | - |
dc.contributor.author | Griffin, Jim E. | - |
dc.contributor.author | Steel, Mark | - |
dc.date.accessioned | 2017-02-14T11:13:09Z | - |
dc.date.available | 2017-02-14T11:13:09Z | - |
dc.date.issued | 2013-09-20 | - |
dc.identifier.citation | Journal of Computational and Graphical Statistics, 2013, vol. 22, no. 3, pp. 729-748 | en_US |
dc.identifier.issn | 10618600 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/9637 | - |
dc.description.abstract | This article describesmethods for efficient posterior simulation for Bayesian variable selection in generalized linear models with many regressors but few observations. The algorithms use a proposal on model space that contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described that allows automatic, efficient computation in these models. The method is applied to examples from normal linear and probit regression. Relevant code and datasets are posted online as supplementary materials. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Journal of Computational and Graphical Statistics | en_US |
dc.rights | © Taylor & Francis | en_US |
dc.subject | Linear regression | en_US |
dc.subject | Metropolis-within-Gibbs | en_US |
dc.subject | Probit regression | en_US |
dc.title | Adaptive Monte Carlo for Bayesian variable selection in regression models | en_US |
dc.type | Article | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | University of Kent at Canterbury | en_US |
dc.collaboration | University of Warwick | en_US |
dc.subject.category | Health Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | Cyprus | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Medical and Health Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1080/10618600.2012.694756 | en_US |
dc.relation.issue | 3 | en_US |
dc.relation.volume | 22 | en_US |
cut.common.academicyear | 2013-2014 | en_US |
dc.identifier.spage | 729 | en_US |
dc.identifier.epage | 748 | en_US |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 1537-2715 | - |
crisitem.journal.publisher | Taylor & Francis | - |
crisitem.author.dept | Department of Nursing | - |
crisitem.author.faculty | Faculty of Health Sciences | - |
crisitem.author.parentorg | Faculty of Health Sciences | - |
Appears in Collections: | Άρθρα/Articles |
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