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MORE ABOUT THIS BOOK
Main description:
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.
Contents:
Relative Risk
and Log-Location-Scale Family.- Bayesian P-Splines.- Discrete Time Models.- Continuous
Time Models.
PRODUCT DETAILS
Publisher: Springer (Springer Spektrum)
Publication date: January, 2015
Pages: 120
Weight: 1657g
Availability: Available
Subcategories: General Issues
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