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