Stander, J;
Dalla Valle, L;
Cortina-Borja, M;
(2018)
A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live?
The American Statistician
, 72
(4)
pp. 368-375.
10.1080/00031305.2017.1328374.
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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 modelling of a data set 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.
| Type: | Article |
|---|---|
| Title: | A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live? |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/00031305.2017.1328374 |
| Publisher version: | http://doi.org/10.1080/00031305.2017.1328374 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | BUGS, Censoring, Frequentist survival analysis, JAGS, Lexis diagram, Model checking, Pope Francis, Posterior predictive distribution, Weibull residuals |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1571046 |
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