Bayesian Analysis of Failure Time Data Using P-Splines
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...
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| フォーマット: | 図書 |
| 言語: | English |
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Springer
2015
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| オンライン･アクセス: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58112 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| 要約: | 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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