Jeffreys priors for survival models with censored data |
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| Affiliation: | 1. Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan;2. Institute of Clinical Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan;3. Department of Internal Medicine, Taipei Veterans General Hospital, Yuan-Shan Branch, I-Lan, Taiwan;4. HB Calleja Heart and Vascular Institute, St. Luke''s Medical Center, Quezon City, Philippines;1. Dalian University of Technology, China;2. Nanjing University of Science and Technology, China |
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| Abstract: | ![]()
When prior information on model parameters is weak or lacking, Bayesian statistical analyses are typically performed with so-called “default” priors. We consider the problem of constructing default priors for the parameters of survival models in the presence of censoring, using Jeffreys’ rule. We compare these Jeffreys priors to the “uncensored” Jeffreys priors, obtained without considering censored observations, for the parameters of the exponential and log-normal models. The comparison is based on the frequentist coverage of the posterior Bayes intervals obtained from these prior distributions. |
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