Bayesian analysis of failure time data using P-Splines [electronic resource] / Matthias Kaeding.
2015
QA276 .K34 2015eb
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Title
Bayesian analysis of failure time data using P-Splines [electronic resource] / Matthias Kaeding.
Author
Kaeding, Matthias, author.
ISBN
9783658083939 electronic book
365808393X electronic book
9783658083922
365808393X electronic book
9783658083922
Published
Wiesbaden : Springer Spektrum, 2015.
Language
English
Description
1 online resource (ix, 110 pages) : illustrations.
Item Number
10.1007/978-3-658-08393-9 doi
Call Number
QA276 .K34 2015eb
Dewey Decimal Classification
519.5/46
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 12, 2015).
Series
BestMasters.
Available in Other Form
Print version: 9783658083922
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Table of Contents
Relative Risk and Log-Location-Scale Family
Bayesian P-Splines
Discrete Time Models
Continuous Time Models.
Bayesian P-Splines
Discrete Time Models
Continuous Time Models.