Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution
In numerous survival analysis experiments, subjects may experience failure or death due to multiple causes. Whether these causes are dependent or independent, this study delves into the competing risk lifetime model under progressively type-II censoring schemes, where removal events follow a binomia...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10988770/ |
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| Summary: | In numerous survival analysis experiments, subjects may experience failure or death due to multiple causes. Whether these causes are dependent or independent, this study delves into the competing risk lifetime model under progressively type-II censoring schemes, where removal events follow a binomial distribution. Specifically, we focus on the generalized power half logistic geometric lifetime failure model in the context of independent causes. We consider the removal of subjects at each failure time according to a binomial distribution with known parameters. Both classical and Bayesian approaches facilitate point- and interval-estimation procedures for parameters and parametric functions. The Bayesian estimate is derived using the Markov Chain Monte Carlo (MCMC) method, incorporating symmetric and asymmetric loss functions. The Metropolis-Hasting algorithm is applied to generate MCMC samples from the posterior density function. A simulated data set is utilized to evaluate the performance of the two estimation approaches under the proposed censoring scheme. In addition, a real dataset is employed for illustrative purposes. |
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| ISSN: | 2169-3536 |