Bayesian Analysis of Failure Time Data Using P-Splines
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...
Đã lưu trong:
| Tác giả chính: | |
|---|---|
| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
Springer
2015
|
| Những chủ đề: | |
| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58112 |
| Các nhãn: |
Thêm thẻ
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
|---|
| id |
oai:scholar.dlu.edu.vn:DLU123456789-58112 |
|---|---|
| record_format |
dspace |
| spelling |
oai:scholar.dlu.edu.vn:DLU123456789-581122023-11-11T06:00:16Z Bayesian Analysis of Failure Time Data Using P-Splines Kaeding, Matthias Failure time data analysis Mathematics Applied 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. 2015-09-09T03:40:07Z 2015-09-09T03:40:07Z 2015 Book 978-3-658-08393-9 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58112 en application/pdf Springer |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Failure time data analysis Mathematics Applied |
| spellingShingle |
Failure time data analysis Mathematics Applied Kaeding, Matthias Bayesian Analysis of Failure Time Data Using P-Splines |
| description |
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. |
| format |
Book |
| author |
Kaeding, Matthias |
| author_facet |
Kaeding, Matthias |
| author_sort |
Kaeding, Matthias |
| title |
Bayesian Analysis of Failure Time Data Using P-Splines |
| title_short |
Bayesian Analysis of Failure Time Data Using P-Splines |
| title_full |
Bayesian Analysis of Failure Time Data Using P-Splines |
| title_fullStr |
Bayesian Analysis of Failure Time Data Using P-Splines |
| title_full_unstemmed |
Bayesian Analysis of Failure Time Data Using P-Splines |
| title_sort |
bayesian analysis of failure time data using p-splines |
| publisher |
Springer |
| publishDate |
2015 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58112 |
| _version_ |
1782550609063313408 |