TY - BOOK UR - http://lib.ugent.be/catalog/ebk01:3710000000324641 ID - ebk01:3710000000324641 LA - eng TI - Bayesian Analysis of Failure Time Data Using P-Splines PY - 2015 SN - 9783658083939 AU - Kaeding, Matthias. author. AB - Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models. AB - 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. ER -Download RIS file
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245 | 1 | a Bayesian Analysis of Failure Time Data Using P-Splines h [electronic resource] / c by Matthias Kaeding. | |
264 | 1 | a Wiesbaden : b Springer Fachmedien Wiesbaden : b Imprint: Springer Spektrum, c 2015. | |
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490 | 1 | a BestMasters | |
505 | a Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models. | ||
520 | a 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. | ||
650 | a Mathematics. | ||
650 | a Laboratory medicine. | ||
650 | a Bioinformatics. | ||
650 | a Probabilities. | ||
650 | 1 | 4 | a Mathematics. |
650 | 2 | 4 | a Probability Theory and Stochastic Processes. |
650 | 2 | 4 | a Laboratory Medicine. |
650 | 2 | 4 | a Bioinformatics. |
710 | 2 | a SpringerLink (Online service) | |
773 | t Springer eBooks | ||
776 | 8 | i Printed edition: z 9783658083922 | |
830 | a BestMasters | ||
856 | 4 | u http://dx.doi.org/10.1007/978-3-658-08393-9 | |
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