In this paper, we focussed on the scale parameter and reliability estimations of the inverse generalized Weibull distribution. Both classical and Bayesian approaches are considered with various loss functions as general entropy, squared log error and weight squared error. For the Bayesian method, both informative and non-informative priors are applied for the reliability and scale parameter estimation. Furthermore, we introduce a new loss function that exhibits some attractive performances. The reliability function and scale parameter of the inverse generalized Weibull distribution are estimated based on the new loss function. By the Monte Carlo simulation procedure, we demonstrate the efficiency of the new proposed loss function among some competitors in estimating the reliability function . Finally, the analysis of two real data set has also been represented for illustration purposes. Some goodness of fit measures affirmed the adequacy of the inverse generalized Weibull distribution in modelling real data sets.

在本文中，我们关注逆广义威布尔分布的尺度参数和可靠性估计。经典和贝叶斯方法都被认为具有各种损失函数，如一般熵、平方对数误差和权重平方误差。对于贝叶斯方法，信息性和非信息性先验都应用于可靠性和尺度参数估计。此外，我们引入了一个新的损失函数，它展示了一些有吸引力的性能。基于新的损失函数估计逆广义威布尔分布的可靠性函数和尺度参数。通过蒙特卡罗模拟程序，我们在一些竞争对手中证明了新提出的损失函数在估计可靠性函数方面的效率。最后，出于说明目的，还展示了对两个真实数据集的分析。一些拟合优度度量证实了逆广义威布尔分布在建模真实数据集时的充分性。