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.
ISBN: | 9783658083922 |
Publication date: | 12th January 2015 |
Author: | Matthias Kaeding |
Publisher: | Springer Spektrum an imprint of Springer Fachmedien Wiesbaden |
Format: | Paperback |
Pagination: | 110 pages |
Series: | BestMasters |
Genres: |
Probability and statistics Stochastics Computational biology / bioinformatics Medical research |