Bayesian Analysis of Failure Time Data Using P-Splines

Kaeding, Matthias

ISBN 10: 3658083921 ISBN 13: 9783658083922

Editorial: Springer Fachmedien Wiesbaden, 2014

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Series: BestMasters. Num Pages: 119 pages, 23 black & white illustrations, 8 black & white tables, biography. BIC Classification: MBGL; PBT; PSD. Category: (P) Professional & Vocational. Dimension: 210 x 148 x 7. Weight in Grams: 171. . 2014. Paperback. . . . . N� de ref. del art�culo V9783658083922

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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.

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

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T�tulo: Bayesian Analysis of Failure Time Data Using...
Editorial: Springer Fachmedien Wiesbaden
A�o de publicaci�n: 2014
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Condici�n: New

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Bayesian Analysis of Failure Time Data Using P-Splines

Kaeding, Matthias

Publicado por Springer, 2015

ISBN 10: 3658083921 ISBN 13: 9783658083922

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Bayesian Analysis of Failure Time Data Using P-Splines

Matthias Kaeding

Publicado por Springer Fachmedien Wiesbaden, 2015

ISBN 10: 3658083921 ISBN 13: 9783658083922

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Condici�n: 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. N� de ref. del art�culo: 5123876

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Bayesian Analysis of Failure Time Data Using P-Splines (BestMasters)

Kaeding, Matthias

Publicado por Springer Spektrum, 2015

ISBN 10: 3658083921 ISBN 13: 9783658083922

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Bayesian Analysis of Failure Time Data Using P-Splines

Matthias Kaeding

Publicado por Springer Fachmedien Wiesbaden, Springer Fachmedien Wiesbaden Jan 2015, 2015

ISBN 10: 3658083921 ISBN 13: 9783658083922

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783658083922

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Bayesian Analysis of Failure Time Data Using P-Splines

Matthias Kaeding

Publicado por Springer Fachmedien Wiesbaden Jan 2015, 2015

ISBN 10: 3658083921 ISBN 13: 9783658083922

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783658083922

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Bayesian Analysis of Failure Time Data Using P-Splines

Matthias Kaeding

Publicado por Springer Fachmedien Wiesbaden, 2015

ISBN 10: 3658083921 ISBN 13: 9783658083922

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783658083922

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