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.

A Bayesian Survival Analysis of an Historical Dataset: How Long Do Popes Live?

Dalla Valle L;
2018-01-01

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.
2018
72
4
368
375
https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1328374
Censoring; Frequentist survival analysis; Posterior predictive distribution
Stander J; Dalla Valle L; Cortina Borja M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2027371
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