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Bayesian Analysis of Failure Time Data Using P-Splines

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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.

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Detail Information

Series Title

-

Call Number

570.285

Publisher

: ., 2015

Collation

IX, 110

Language

English

ISBN/ISSN

978-3-658-08393-9

Classification

570.285

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Bioinformatics

Specific Detail Info

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Statement of Responsibility

Matthias Kaeding

Other Information

Cataloger

ratna

Source

https://link.springer.com/10.1007/978-3-658-08393-9

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