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.
About the Author
Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
Table of ContentsRelative Risk and Log-Location-Scale Family.- Bayesian P-Splines.- Discrete Time Models.- Continuous