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Bayesian inference for Birnbaum-Saunders distribution and its generalization

机译:Birnbaum-Saunders分布的贝叶斯推断及其推广

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摘要

We present a Bayesian approach for parameter inference of the Birnbaum-Saunders distribution [Birnbaum ZW, Saunders SC. A new family of life distributions. J Appl Probab. 1969;6:319-327], as well as the generalized Birnbaum-Saunders distribution developed by Owen [A new three-parameter extension to the Birnbaum-Saunders distribution. IEEE Trans Reliab. 2006;55:475-479], in the presence of random right-censored data. To handle the instance of commonly occurred censored observations, we utilize the data augmentation technique [Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation. J Amer Statist Assoc. 1987;82(398):528-540] to circumvent the arduous expressions involving the censored data in posterior inferences. Simulation studies are carried out to assess performance of these methods under different parameter values, with small and large sample sizes, as well as various degrees of censoring. Two real data are analysed for illustrative purpose.
机译:我们为Birnbaum-Saunders分布的参数推断提供一种贝叶斯方法[Birnbaum ZW,Saunders SC。一个新的生活分布族。 J Appl Probab。 1969; 6:319-327],以及Owen开发的广义Birnbaum-Saunders分布[Birnbaum-Saunders分布的新三参数扩展。 IEEE Trans Reliab。 2006; 55:475-479],则存在随机右删失数据。为了处理常见的审查观测的实例,我们利用数据增强技术[Tanner MA,Wong WH。通过数据扩充计算后验分布。 J Amer统计学家协会。 1987; 82(398):528-540]来规避后验推断中涉及删失数据的艰巨表达式。进行仿真研究以评估这些方法在不同参数值下(无论样本大小如何)以及不同程度的审查的性能。为了说明目的,分析了两个真实数据。

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