In this article, several independent populations following exponential distribution with common location parameter and unknown and unequal scale parameters are considered. From these populations, several independent samples of generalized order statistics (gos) are drawn. Under the setup of gos, the problem of estimation of common location parameter is discussed and various estimators of common location parameter are derived. The authors obtained maximum likelihood estimator (MLE), modified MLE and uniformly minimum variance unbiased estimator of common location parameter. Furthermore, under scaled-squared error loss function, a general inadmissibility result of invariant estimator is proposed. The derived results are further reduced for upper record values which is a special case of gos. Finally, simulation study and real life example are reported to show the performances of various competing estimators in terms of percentage risk improvement.

在本文中，考虑了几个具有共同位置参数和未知和不等尺度参数的指数分布的独立种群。从这些总体中，抽取了几个独立的广义顺序统计 ( gos ) 样本。在gos的设置下，讨论了公共位置参数的估计问题，推导了公共位置参数的各种估计量。作者获得了公共位置参数的最大似然估计量 (MLE)、改进的 MLE 和均匀最小方差无偏估计量。此外，在尺度平方误差损失函数下，提出了不变量估计量的一般不可接受性结果。对于较高的记录值，派生的结果会进一步减少，这是一种特殊情况去。最后，报告模拟研究和现实生活中的例子，以显示各种竞争估计器在百分比风险改进方面的表现。