Titlebook: Bayesian Inference and Computation in Reliability and Survival Analysis; Yuhlong Lio,Ding-Geng Chen,Tzong-Ru Tsai Book 2022 The Editor(s)

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Bayesian Analysis of a New Bivariate Wiener Degradation Processlifetime have analytic forms and are presented in this chapter. Statistical inference is conducted by data augmentation and Bayesian methods. Weak informative priors are considered, and Gibbs sampling method is utilized to draw sample for the evaluation of the unknown parameters. A simulated example is used for illustration purpose.

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A Bayesian Approach for the Analysis of Tumorigenicity Data from Sacrificial Experiments Under Weibuthe performance of the developed Bayesian approach with different priors. A comparison is also made with the likelihood estimates determined from an EM algorithm. Finally, a known mice tumor toxicology dataset is analyzed to illustrate the developed Bayesian approach.

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Jasmine Grabher,Madeleine Grawehrs like relapse and a terminal event like death. We develop Bayesian methods to analyze clustered data under the semi-competing risks framework. Subsequently, R program codes are provided to analyze publically available breast cancer data. Parameter estimations are performed based on Gibbs sampling within Metropolis–Hastings algorithm.

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Bayesian Analysis for Clustered Data under a Semi-Competing Risks Frameworks like relapse and a terminal event like death. We develop Bayesian methods to analyze clustered data under the semi-competing risks framework. Subsequently, R program codes are provided to analyze publically available breast cancer data. Parameter estimations are performed based on Gibbs sampling within Metropolis–Hastings algorithm.