Bayesian Analysis of Failure Time Data Using P-Splines

2015
ISBN: 978-3-658-08392-2
Verlag: Springer

Buch, Englisch, 110 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 1657 g

Reihe: BestMasters

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.

Zielgruppe

Research

Autoren/Hrsg.

Kaeding, Matthias

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik

Inhaltsverzeichnis

Relative Risk
and Log-Location-Scale Family.- Bayesian P-Splines.- Discrete Time Models.- Continuous
Time Models.

Über Autor(innen)

Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.