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 |
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.
Bayesian Analysis of Failure Time Data Using P-Splines features in the following genres: Probability and statistics, Stochastics, Computational biology / bioinformatics, Medical research
Paperback. Not Available.
Bayesian Analysis of Failure Time Data Using P-Splines was written by Matthias Kaeding and published by Springer Spektrum an imprint of Springer Fachmedien Wiesbaden
Bayesian Analysis of Failure Time Data Using P-Splines has 110 pages
Yes it is part of BestMasters series