Skip to main content
SpringerLink
Log in

Bayesian Analysis of Failure Time Data Using P-Splines

  • Book
  • © 2015

Overview

Authors:
  1. Matthias Kaeding

    1. Hamburg, Germany

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Publication in the field of natural sciences
  • Includes supplementary material: sn.pub/extras

Part of the book series: BestMasters (BEST)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (8 chapters)

  1. Front Matter

    Pages I-IX
    Download chapter PDF

  2. Introduction

    • Matthias Kaeding
    Pages 1-4

  3. Basic Concepts of Failure Time Analysis

    • Matthias Kaeding
    Pages 5-16

  4. Computation and Inference

    • Matthias Kaeding
    Pages 17-44

  5. Discrete Time Models

    • Matthias Kaeding
    Pages 45-59

  6. Application I: Unemployment Durations

    • Matthias Kaeding
    Pages 61-68

  7. Continuous Time Models

    • Matthias Kaeding
    Pages 69-85

  8. Application II: Crime Recidivism

    • Matthias Kaeding
    Pages 87-94

  9. Summary and Outlook

    • Matthias Kaeding
    Pages 95-97

  10. Back Matter

    Pages 99-110
    Download chapter PDF
Back to top

Keywords

About this book

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.

Authors and Affiliations

  • Hamburg, Germany

    Matthias Kaeding

About the author

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

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation