In this study, a family of distributions called cubic lower record-based transmuted is provided. A special case of this family is proposed as an alternative exponential distribution. Several statistical properties are explored. We utilize nine different methods to estimate the parameters of the suggested distribution. In order to compare the performances of these methods, we consider a comprehensive Monte-Carlo simulation study. As a result of simulation study, we conclude that minimum absolute distance estimator is a valuable alternative to maximum likelihood estimator. Then, we carried out two real-world data examples to evaluate the fits of introduced distribution as well as its potential competitor ones. The findings of real-world data analysis show that the best-fitting distribution for both datasets is our model.

翻译：本研究提出了一种称为立方低记录变换的分布族。作为该分布族的一个特例，我们提出了一种替代性指数分布。本文探讨了该分布的若干统计性质。我们采用九种不同方法对建议分布的参数进行估计。为比较这些方法的性能，我们进行了全面的蒙特卡洛模拟研究。模拟结果表明，最小绝对距离估计量是极大似然估计量的有效替代方法。随后，我们通过两个实际数据案例评估了所提出分布及其潜在竞争模型的拟合效果。实际数据分析表明，对于两个数据集，最优拟合分布均为我们提出的模型。