Bayesian Analysis of Failure Time Data Using P-Splines
Matthias Kaeding
ISBN 10: 3658083921 ISBN 13: 9783658083922
Published by Springer Fachmedien Wiesbaden, 2015
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Bayesian Analysis of Failure Time Data Using P-Splines | Matthias Kaeding | Taschenbuch | ix | Englisch | 2015 | Springer Fachmedien Wiesbaden | EAN 9783658083922 | Verantwortliche Person für die EU: Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Seller Inventory # 105010039
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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.
About the Author: Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
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Title: Bayesian Analysis of Failure Time Data Using...
Publisher: Springer Fachmedien Wiesbaden
Publication Date: 2015
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Taschenbuch. Condition: Neu. Neuware -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.Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg 120 pp. Englisch. Seller Inventory # 9783658083922
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 120 pp. Englisch. Seller Inventory # 9783658083922
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