改进自适应渐进 II 型删失下 Chen 分布的贝叶斯分析
Bayesian Analysis of Chen Distributionunder Improved Adaptive ProgressiveType-II Censoring
摘要: 本文基于改进的自适应渐进 II 型删失，对 Chen 分布进行了贝叶斯分析。首先利用 EM 算法得 到了参数的极大似然估计。针对共轭和非共轭的四种信息先验，运用大方差和遗传算法确定了先 验的超参数。进而根据 Metropolis-Hastings 算法实现了后验分布样本的抽取。最后，通过真实 数据集对不同先验下的贝叶斯估计性能进行比较并得出了相应的结论。
Abstract: This paper presents a Bayesian analysis of the Chen distribution based on an improved adaptive progressive Type-II censoring. Initially, the EM algorithm is used to obtain the maximum likelihood estimation of the parameters. Four types of informative priors, including conjugate and non-conjugate, are then determined using large variance and genetic algorithms. Subsequently, the Metropolis-Hastings algorithm is employed to extract samples from the posterior distribution. Finally, a comparison of the Bayesian estimation performance under different priors is conducted using real datasets, leading to corresponding conclusions.
文章引用：张莉. 改进自适应渐进 II 型删失下 Chen 分布的贝叶斯分析[J]. 理论数学, 2024, 14(1): 400-415. https://doi.org/10.12677/PM.2024.141040

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