LEADER 03330nam  22006975  450 
001 9910304132703321
005 20230810213508.0
010   $a3-658-08393-X
024 7 $a10.1007/978-3-658-08393-9
035   $a(CKB)3710000000324641
035   $a(EBL)1967643
035   $a(OCoLC)908087543
035   $a(SSID)ssj0001407824
035   $a(PQKBManifestationID)11807636
035   $a(PQKBTitleCode)TC0001407824
035   $a(PQKBWorkID)11411345
035   $a(PQKB)10651393
035   $a(DE-He213)978-3-658-08393-9
035   $a(MiAaPQ)EBC1967643
035   $a(PPN)183151496
035   $a(EXLCZ)993710000000324641
100   $a20141226d2015      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aBayesian Analysis of Failure Time Data Using P-Splines /$fby Matthias Kaeding
205   $a1st ed. 2015.
210  1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Spektrum,$d2015.
215   $a1 online resource (117 p.)
225 1 $aBestMasters,$x2625-3615
300   $aDescription based upon print version of record.
311   $a3-658-08392-1 
320   $aIncludes bibliographical references.
327   $aRelative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models.
330   $aMatthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model. Contents Relative Risk and Log-Location-Scale Family Bayesian P-Splines Discrete Time Models Continuous Time Models Target Groups Researchers and students in the fields of statistics, engineering, and life sciences Practitioners in the fields of reliability engineering and data analysis involved with lifetimes The Author Matthias Kaeding obtained his Master of Science degree at the University of Bamberg in Survey Statistics.
410  0$aBestMasters,$x2625-3615
606   $aProbabilities
606   $aMedicine$xResearch
606   $aBiology$xResearch
606   $aBioinformatics
606   $aProbability Theory
606   $aBiomedical Research
606   $aBioinformatics
615  0$aProbabilities.
615  0$aMedicine$xResearch.
615  0$aBiology$xResearch.
615  0$aBioinformatics.
615 14$aProbability Theory.
615 24$aBiomedical Research.
615 24$aBioinformatics.
676   $a510
676   $a519.2
676   $a570285
676   $a610724
700   $aKaeding$b Matthias$4aut$4http://id.loc.gov/vocabulary/relators/aut$0893250
906   $aBOOK
912   $a9910304132703321
996   $aBayesian Analysis of Failure Time Data Using P-Splines$91995411
997   $aUNINA