Parametric Models

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Debajyoti Sinha
Part of the Springer Series in Statistics book series (SSS)

Abstract

Parametric models play an important role in Bayesian survival analysis, since many Bayesian analyses in practice are carried out using parametric models. Parametric modeling offers straightforward modeling and analysis techniques. In this chapter, we discuss parametric models for univariate right censored survival data. We derive the posterior and predictive distributions and demonstrate how to carry out Bayesian analyses for several commonly used parametric models. The statistical literature in Bayesian parametric survival analysis and life-testing is too enormous to list here, but some references dealing with applications to medicine or public health include Grieve (1987), Achcar, Bolfarine, and Pericchi (1987), Achcar, Bookmeyer, and Hunter (1985), Chen, Hill, Greenhouse, and Fayos (1985), Dellaportas and Smith (1993), and Kim and Ibrahim (2001).

Keywords

Posterior Distribution Markov Chain Monte Carlo Markov Chain Monte Carlo Method Weibull Model Joint Posterior Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Debajyoti Sinha
    • 3
  1. 1.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  2. 2.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Department of Biometry and EpidemiologyMedical Universtiy of South CarolinaCharlestonUSA

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