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Bayesian analysis of failure time data using P-Splines / Matthias Kaeding.

By: Series: BestMastersPublication details: Wiesbaden : Springer Spektrum, 2015.Description: ix, 110 p. ; illustrationsISBN:
  • 9783658083922
Subject(s): DDC classification:
  • 000SA.06 23 K11
Contents:
1. Introduction-- 2. Basic concepts of Failure time analysis-- 3. Computation and inference-- 4. Discrete time models-- 5. Application I: Unemployment durations-- 6. Continuous time models-- 7. Application II: Crime recidivism-- 8. Summary and outlook-- Appendix A: Description of R function-- Bibliography.
Summary: 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 Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.06 K11 (Browse shelf(Opens below)) Available 136111
Total holds: 0

Includes bibliographical references.

1. Introduction--
2. Basic concepts of Failure time analysis--
3. Computation and inference--
4. Discrete time models--
5. Application I: Unemployment durations--
6. Continuous time models--
7. Application II: Crime recidivism--
8. Summary and outlook--
Appendix A: Description of R function--
Bibliography.

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

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