Power function is amougst the most suitable probability models for survival or failure times analysis, particularly of electronic components and product reliability. The article proposes some new modified moment estimators for parameter estimation of the power function distribution. The proposed estimators are based on some non-conventional descriptive measures like harmonic mean, quartile deviation, Shannon entropy and Gini index. The performance of the proposed estimators is compared with the traditional moment and existing modified moment estimators. Performance is assessed through the Monte Carlo simulation and three real-life data sets representing failure and survival times of components and infected animals, respectively. Some common accuracy measures are used as performance indicators. From both, Monte Carlo simulation and all real-life applications, the results show better performance of proposed modified moment estimators based on the Gini index and harmonic mean. Hence, the use of these modified estimators is recommended for parameter estimation of the power function distribution.

幂函数是最适合用于生存或故障时间分析的概率模型，尤其是电子元件和产品可靠性分析。本文提出了一些新的修正矩估计器，用于幂函数分布的参数估计。建议的估计量基于一些非常规的描述性度量，如调和平均值、四分位数偏差、香农熵和基尼指数。将所提出的估计器的性能与传统矩和现有的修改矩估计器进行比较。性能通过蒙特卡罗模拟和三个分别代表组件和受感染动物的故障和存活时间的真实数据集进行评估。一些常见的准确度度量被用作性能指标。从两者，蒙特卡罗模拟和所有实际应用，结果表明基于基尼指数和调和平均数的改进矩估计器具有更好的性能。因此，推荐使用这些修改的估计器来估计幂函数分布的参数。