Bayesian Analysis of Failure Time Data Using P-Splines

Beschreibung

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

Target Groups

• Researchers and students in the fields of statistics, engineering, and life sciences
• Practitioners in the fields of reliability engineering and data analysis involved with lifetimes

The Author

Autor*in

Matthias Kaeding

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Bayesian Statistics Cox Modell Failure Time Analysis Gibbs Sampler IWLS Proposals

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