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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.
| Autorius: | Matthias Kaeding |
| Serija: | BestMasters |
| Leidėjas: | Springer Fachmedien Wiesbaden |
| Išleidimo metai: | 2015 |
| Knygos puslapių skaičius: | 120 |
| ISBN-10: | 3658083921 |
| ISBN-13: | 9783658083922 |
| Formatas: | 210 x 148 x 7 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Bayesian Analysis of Failure Time Data Using P-Splines“
