Adaptive Type-II progressive hybrid censoring scheme has been proposed to increase the efficiency of statistical analysis and save the total test time on a life-testing experiment. This article deals with the problem of estimating the parameters, survival and hazard rate functions of the two-parameter Hjorth distribution under adaptive Type-II progressive hybrid censoring scheme using maximum likelihood and Bayesian approaches. The two-sided approximate confidence intervals of the unknown quantities are constructed. Under the assumption of independent gamma priors, the Bayes estimators are obtained using squared error loss function. Since the Bayes estimators cannot be expressed in closed forms, Lindley’s approximation and Markov chain Monte Carlo methods are considered and the highest posterior density credible intervals are also obtained. To study the behavior of the various estimators, a Monte Carlo simulation study is performed. The performances of the different estimators have been compared on the basis of their average root mean squared error and relative absolute bias. Finally, to show the applicability of the proposed estimators a data set of industrial devices has been analyzed.

提出了自适应 II 类渐进式混合删失方案，以提高统计分析的效率并节省寿命测试实验的总测试时间。本文讨论了使用最大似然和贝叶斯方法在自适应 II 类渐进混合审查方案下估计双参数 Hjorth 分布的参数、生存和风险率函数的问题。构造未知量的两侧近似置信区间。在独立伽马先验假设下，使用平方误差损失函数获得贝叶斯估计量。由于贝叶斯估计量不能以封闭形式表示，因此考虑了林德利近似和马尔可夫链蒙特卡罗方法，并获得了最高后验密度可信区间。为了研究各种估计器的行为，进行了蒙特卡罗模拟研究。已经根据它们的平均均方根误差和相对绝对偏差比较了不同估计器的性能。最后，为了显示所提出的估算器的适用性，我们分析了工业设备的数据集。