語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Bayesian analysis of failure time da...
~
Kaeding, Matthias.
FindBook
Google Book
Amazon
博客來
Bayesian analysis of failure time data using P-Splines
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Bayesian analysis of failure time data using P-Splines/ by Matthias Kaeding.
作者:
Kaeding, Matthias.
出版者:
Wiesbaden :Springer Fachmedien Wiesbaden : : 2015.,
面頁冊數:
ix, 110 p. :ill., digital ;24 cm.
內容註:
Relative Risk and Log-Location-Scale Family -- Bayesian P-Splines -- Discrete Time Models -- Continuous Time Models.
Contained By:
Springer eBooks
標題:
Failure time data analysis. -
電子資源:
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:
533217
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
1994405
003
DE-He213
005
20150729111753.0
006
m d
007
cr nn 008maaau
008
151019s2015 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
2133202
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
533217
650
0
$a
Bayesian statistical decision theory.
$3
551404
650
1 4
$a
Mathematics.
$3
515831
650
2 4
$a
Probability Theory and Stochastic Processes.
$3
891080
650
2 4
$a
Laboratory Medicine.
$3
894271
650
2 4
$a
Bioinformatics.
$3
553671
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer eBooks
830
0
$a
BestMasters.
$3
2056364
856
4 0
$u
http://dx.doi.org/10.1007/978-3-658-08393-9
950
$a
Behavioral Science (Springer-11640)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9267108
電子資源
11.線上閱覽_V
電子書
EB QA276
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入