A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times
We present a Bayesian approach for analysis of competing risks survival data with masked causes of failure. This approach is often used to assess the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time...
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2020-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8248640 |
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doaj-79741e06162e44fd9cdffa162ba5376a2020-11-25T02:55:48ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/82486408248640A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure TimesYosra Yousif0Faiz A. M. Elfaki1Meftah Hrairi2Oyelola A. Adegboye3Department of Mechanical Engineering, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur, MalaysiaDepartment of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha, QatarDepartment of Mechanical Engineering, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur, MalaysiaEvolution Equations Research Group, Ton Duc Thang University, Ho Chi Minh City, VietnamWe present a Bayesian approach for analysis of competing risks survival data with masked causes of failure. This approach is often used to assess the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time for the remaining subjects. Such data, known as partly interval-censored data, usually result from periodic inspection in production engineering. In this study, Dirichlet and Gamma processes are assumed as priors for masking probabilities and baseline hazards. Markov chain Monte Carlo (MCMC) technique is employed for the implementation of the Bayesian approach. The effectiveness of the proposed approach is illustrated with simulated and production engineering applications.http://dx.doi.org/10.1155/2020/8248640 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Yosra Yousif Faiz A. M. Elfaki Meftah Hrairi Oyelola A. Adegboye |
| spellingShingle |
Yosra Yousif Faiz A. M. Elfaki Meftah Hrairi Oyelola A. Adegboye A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times Mathematical Problems in Engineering |
| author_facet |
Yosra Yousif Faiz A. M. Elfaki Meftah Hrairi Oyelola A. Adegboye |
| author_sort |
Yosra Yousif |
| title |
A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times |
| title_short |
A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times |
| title_full |
A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times |
| title_fullStr |
A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times |
| title_full_unstemmed |
A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times |
| title_sort |
bayesian approach to competing risks model with masked causes of failure and incomplete failure times |
| publisher |
Hindawi Limited |
| series |
Mathematical Problems in Engineering |
| issn |
1024-123X 1563-5147 |
| publishDate |
2020-01-01 |
| description |
We present a Bayesian approach for analysis of competing risks survival data with masked causes of failure. This approach is often used to assess the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time for the remaining subjects. Such data, known as partly interval-censored data, usually result from periodic inspection in production engineering. In this study, Dirichlet and Gamma processes are assumed as priors for masking probabilities and baseline hazards. Markov chain Monte Carlo (MCMC) technique is employed for the implementation of the Bayesian approach. The effectiveness of the proposed approach is illustrated with simulated and production engineering applications. |
| url |
http://dx.doi.org/10.1155/2020/8248640 |
| work_keys_str_mv |
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1715349558192504832 |