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dc.contributor.authorStander, J
dc.contributor.authorDalla Valle, L
dc.contributor.authorCortina Borja, M
dc.date.accessioned2017-04-26T20:41:01Z
dc.date.accessioned2017-05-05T15:31:06Z
dc.date.available2017-04-26T20:41:01Z
dc.date.available2017-05-05T15:31:06Z
dc.date.issued2017-08-27
dc.identifier.issn0003-1305
dc.identifier.issn1537-2731
dc.identifier.urihttp://hdl.handle.net/10026.1/9194
dc.description.abstract

University courses in statistical modeling often place great emphasis on methodological theory, illustrating it only briefly by means of limited and repeatedly used standard examples. Unfortunately, this approach often fails to actively engage and motivate students in their learning process. The teaching of statistical topics such as Bayesian survival analysis can be enhanced by focusing on innovative applications. Here, we discuss the visualization and modeling of a dataset of historical events comprising the post-election survival times of popes. Inference, prediction, and model checking are performed in the Bayesian framework, with comparisons being made with the frequentist approach. Further opportunities for similar statistical investigations are outlined. Supplementary materials for this article are available online.

dc.format.extent368-375
dc.languageen
dc.language.isoen
dc.publisherTaylor & Francis
dc.relation.replaceshttp://hdl.handle.net/10026.1/9127
dc.relation.replaces10026.1/9127
dc.subjectBUGS
dc.subjectCensoring
dc.subjectFrequentist survival analysis
dc.subjectJAGS
dc.subjectLexis diagram
dc.subjectModel checking
dc.subjectPope Francis
dc.subjectPosterior predictive distribution
dc.subjectWeibull residuals
dc.titleA Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live?
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000452054400010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue4
plymouth.volume72
plymouth.publication-statusPublished
plymouth.journalThe American statistician
dc.identifier.doi10.1080/00031305.2017.1328374
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Admin Group - REF
plymouth.organisational-group/Plymouth/Admin Group - REF/REF Admin Group - FoSE
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2017-04-26
dc.rights.embargodate2018-6-26
dc.identifier.eissn1537-2731
rioxxterms.versionofrecord10.1080/00031305.2017.1328374
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-08-27
rioxxterms.typeJournal Article/Review


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