國立高雄大學圖資館 |

Language: English

Back

Bayesian analysis of failure time da...

Kaeding, Matthias.

Bayesian analysis of failure time data using P-Splines

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian analysis of failure time data using P-Splinesby Matthias Kaeding.
Author:	Kaeding, Matthias.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden :2015.
Description:	ix, 110 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Failure time data analysis.
Online resource:	http://dx.doi.org/10.1007/978-3-658-08393-9
ISBN:	9783658083939 (electronic bk.)

Bayesian analysis of failure time data using P-Splines
Kaeding, Matthias.

Bayesian analysis of failure time data using P-Splines[electronic resource] /by Matthias Kaeding. - Wiesbaden :Springer Fachmedien Wiesbaden :2015. - ix, 110 p. :ill., digital ;24 cm. - BestMasters. - BestMasters..

Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models.

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. Contents Relative Risk and Log-Location-Scale Family Bayesian P-Splines Discrete Time Models Continuous Time Models Target Groups Researchers and students in the fields of statistics, engineering, and life sciences Practitioners in the fields of reliability engineering and data analysis involved with lifetimes The Author Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.

ISBN: 9783658083939 (electronic bk.)

Standard No.: 10.1007/978-3-658-08393-9doiSubjects--Topical Terms:

182533
Failure time data analysis.

LC Class. No.: QA276

Dewey Class. No.: 519.546

Bayesian analysis of failure time data using P-Splines
LDR:02225nmm a2200337 a 4500 001 460940
003 DE-He213
005 20150729111753.0
006 m d
007 cr nn 008maaau
008 151110s2015 gw s 0 eng d
020 $a 9783658083939 (electronic bk.)
020 $a 9783658083922 (paper)
024 7 $a 10.1007/978-3-658-08393-9 $2 doi
035 $a 978-3-658-08393-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.546 $2 23
090 $a QA276 $b .K11 2015
100 1 $a Kaeding, Matthias. $3 712725
245 1 0 $a Bayesian analysis of failure time data using P-Splines $h [electronic resource] / $c by Matthias Kaeding.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Spektrum, $c 2015.
300 $a ix, 110 p. : $b ill., digital ; $c 24 cm.
490 1 $a BestMasters
505 0 $a Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models.
520 $a 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. Contents Relative Risk and Log-Location-Scale Family Bayesian P-Splines Discrete Time Models Continuous Time Models Target Groups Researchers and students in the fields of statistics, engineering, and life sciences Practitioners in the fields of reliability engineering and data analysis involved with lifetimes The Author Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
650 0 $a Failure time data analysis. $3 182533
650 0 $a Bayesian statistical decision theory. $3 182005
650 1 4 $a Mathematics. $3 184409
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Laboratory Medicine. $3 276727
650 2 4 $a Bioinformatics. $3 194415
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 676593
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-08393-9
950 $a Behavioral Science (Springer-11640)