A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live?
dc.contributor.author | Stander, J | |
dc.contributor.author | Dalla Valle, L | |
dc.contributor.author | Cortina Borja, M | |
dc.date.accessioned | 2017-04-26T20:41:01Z | |
dc.date.accessioned | 2017-05-05T15:31:06Z | |
dc.date.available | 2017-04-26T20:41:01Z | |
dc.date.available | 2017-05-05T15:31:06Z | |
dc.date.issued | 2017-08-27 | |
dc.identifier.issn | 0003-1305 | |
dc.identifier.issn | 1537-2731 | |
dc.identifier.uri | http://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.extent | 368-375 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Taylor & Francis | |
dc.relation.replaces | http://hdl.handle.net/10026.1/9127 | |
dc.relation.replaces | 10026.1/9127 | |
dc.subject | BUGS | |
dc.subject | Censoring | |
dc.subject | Frequentist survival analysis | |
dc.subject | JAGS | |
dc.subject | Lexis diagram | |
dc.subject | Model checking | |
dc.subject | Pope Francis | |
dc.subject | Posterior predictive distribution | |
dc.subject | Weibull residuals | |
dc.title | A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live? | |
dc.type | journal-article | |
dc.type | Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000452054400010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 4 | |
plymouth.volume | 72 | |
plymouth.publication-status | Published | |
plymouth.journal | The American statistician | |
dc.identifier.doi | 10.1080/00031305.2017.1328374 | |
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.dateAccepted | 2017-04-26 | |
dc.rights.embargodate | 2018-6-26 | |
dc.identifier.eissn | 1537-2731 | |
rioxxterms.versionofrecord | 10.1080/00031305.2017.1328374 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2017-08-27 | |
rioxxterms.type | Journal Article/Review |