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 fu… Altro …
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.; PDF; Scientific, Technical and Medical > Medicine: general issues > Medical equipment & techniques > Medi, Springer Berlin Heidelberg<
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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 fu… Altro …
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<
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Bayesian Analysis of Failure Time Data Using P-Splines: ab 53.49 € eBooks > Fachthemen & Wissenschaft > Wissenschaften allgemein Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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No. 9783658083939. Costi di spedizione:Instock, Despatched same working day before 3pm, zzgl. Versandkosten., Costi di spedizione aggiuntivi Details...
(*) Libro esaurito significa che il libro non è attualmente disponibile in una qualsiasi delle piattaforme associate che di ricerca.
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 fu… Altro …
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.; PDF; Scientific, Technical and Medical > Medicine: general issues > Medical equipment & techniques > Medi, Springer Berlin Heidelberg<
No. 9783658083939. Costi di spedizione:Instock, Despatched same working day before 3pm, zzgl. Versandkosten., Costi di spedizione aggiuntivi
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 fu… Altro …
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<
Nr. 978-3-658-08393-9. Costi di spedizione:Worldwide free shipping, , DE. (EUR 0.00)
Bayesian Analysis of Failure Time Data Using P-Splines: ab 53.49 € eBooks > Fachthemen & Wissenschaft > Wissenschaften allgemein Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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Informazioni dettagliate del libro - Bayesian Analysis of Failure Time Data Using P-Splines
EAN (ISBN-13): 9783658083939 Anno di pubblicazione: 2014 Editore: Springer Fachmedien Wiesbaden GmbH
Libro nella banca dati dal 2015-08-11T01:13:55+02:00 (Zurich) Pagina di dettaglio ultima modifica in 2023-08-22T15:38:02+02:00 (Zurich) ISBN/EAN: 9783658083939
ISBN - Stili di scrittura alternativi: 978-3-658-08393-9 Stili di scrittura alternativi e concetti di ricerca simili: Autore del libro : kaeding, tao Titolo del libro: data, new bayesian, bayes, tim, time, failure analysis
Dati dell'editore
Autore: Matthias Kaeding Titolo: BestMasters; Bayesian Analysis of Failure Time Data Using P-Splines Editore: Springer Spektrum; Springer Fachmedien Wiesbaden GmbH 110 Pagine Anno di pubblicazione: 2014-12-26 Wiesbaden; DE Stampato / Fatto in Lingua: Inglese 53,49 € (DE) 55,00 € (AT) 59,00 CHF (CH) Available IX, 110 p. 23 illus.
EA; E107; eBook; Nonbooks, PBS / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Bayesian Statistics; Cox Modell; Failure Time Analysis; Gibbs Sampler; IWLS Proposals; C; Probability Theory and Stochastic Processes; Laboratory Medicine; Bioinformatics; Probability Theory; Biomedical Research; Bioinformatics; Behavioral Science and Psychology; Stochastik; Medizinische Forschung; DV-gestützte Biologie/Bioinformatik; BC
Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
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