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Kaeding, Matthias

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.

Details

Published by: Springer Spektrum

Publication Date: 2015-01-12

Format: Paperback

ISBN-13: 9783658083922

DOI: 10.1007/978-3-658-08393-9

Dimensions: 210cm x148cm

Pages: 110