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.
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