Bayesian Analysis of Failure Time Data Using P-Splines

  • MatthiasĀ Kaeding

Part of the BestMasters book series (BEST)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Matthias Kaeding
    Pages 1-4
  3. Matthias Kaeding
    Pages 5-16
  4. Matthias Kaeding
    Pages 17-44
  5. Matthias Kaeding
    Pages 45-59
  6. Matthias Kaeding
    Pages 61-68
  7. Matthias Kaeding
    Pages 69-85
  8. Matthias Kaeding
    Pages 87-94
  9. Matthias Kaeding
    Pages 95-97
  10. Back Matter
    Pages 99-110

About this book


Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model.


  • Relative Risk and Log-Location-Scale Family
  • Bayesian P-Splines
  • Discrete Time Models
  • Continuous Time Models

Target Groups

  • Researchers and students in the fields of statistics, engineering, and life sciences
  • Practitioners in the fields of reliability engineering and data analysis involved with lifetimes

The Author

Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.


Bayesian Statistics Cox Modell Failure Time Analysis Gibbs Sampler IWLS Proposals

Authors and affiliations

  • MatthiasĀ Kaeding
    • 1
  1. 1.HamburgGermany

Bibliographic information

  • DOI
  • Copyright Information Springer Fachmedien Wiesbaden 2015
  • Publisher Name Springer Spektrum, Wiesbaden
  • eBook Packages Behavioral Science
  • Print ISBN 978-3-658-08392-2
  • Online ISBN 978-3-658-08393-9
  • Buy this book on publisher's site
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