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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. Codice articolo 105010039
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.
Informazioni sull?autore: Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
Titolo: Bayesian Analysis of Failure Time Data Using...
Casa editrice: Springer Fachmedien Wiesbaden
Data di pubblicazione: 2015
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Publication in the field of natural sciencesRelative Riskand Log-Location-Scale Family.- Bayesian P-Splines.- Discrete Time Models.- ContinuousTime Models.Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure t. Codice articolo 5123876
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Taschenbuch. Condizione: 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. Codice articolo 9783658083922
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Taschenbuch. Condizione: 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. Codice articolo 9783658083922
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9783658083922
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