Bayesian Analysis of Failure Time Data Using P-Splines

Matthias Kaeding

ISBN 10: 3658083921 / ISBN 13: 9783658083922

Published by Springer Fachmedien Wiesbaden Jan 2015, 2015

About this Item

This item is printed on demand - Print on Demand Titel. 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-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch.

Seller Inventory # 9783658083922

Bibliographic Details

• Title: Bayesian Analysis of Failure Time Data Using P-Splines
• Publisher: Springer Fachmedien Wiesbaden Jan 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 3658083921
• ISBN 13: 9783658083922
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 7D millimeters 210 millimeters width by 148 millimeters height by 7 millimeters depth
• Weight: 167 grams

About this title

Synopsis

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.