期刊全稱 | Bayesian Analysis of Failure Time Data Using P-Splines | 影響因子2023 | Matthias Kaeding | 視頻video | http://file.papertrans.cn/182/181826/181826.mp4 | 發(fā)行地址 | Publication in the field of natural sciences.Includes supplementary material: | 學(xué)科分類 | BestMasters | 圖書封面 |  | 影響因子 | 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. | Pindex | Book 2015 |
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