In the statistical literature, several discrete distributions have been developed so far. However, in this progressive technological era, the data generated from different fields is getting complicated day by day, making it difficult to analyze this real data through the various discrete distributions available in the existing literature. In this context, we have proposed a new flexible family of discrete models named discrete odd Weibull-G (DOW-G) family. Its several impressive distributional characteristics are derived. A key feature of the proposed family is its failure rate function that can take a variety of shapes for distinct values of the unknown parameters, like decreasing, increasing, constant, J-, and bathtub-shaped. Furthermore, the presented family not only adequately captures the skewed and symmetric data sets, but it can also provide a better fit to equi-, over-, under-dispersed data. After producing the general class, two particular distributions of the DOW-G family are extensively studied. The parameters estimation of the proposed family, are explored by the method of maximum likelihood and Bayesian approach. A compact Monte Carlo simulation study is performed to assess the behavior of the estimation methods. Finally, we have explained the usefulness of the proposed family by using two different real data sets.

在统计文献中，迄今为止已经开发了几种离散分布。然而，在这个进步的技术时代，来自不同领域的数据日益复杂，使得通过现有文献中可用的各种离散分布来分析这些真实数据变得困难。在这种情况下，我们提出了一个新的灵活的离散模型族，称为离散奇数 Weibull-G (DOW-G) 族。它的几个令人印象深刻的分布特征被推导出来。所提议系列的一个关键特征是它的故障率函数，它可以针对未知参数的不同值采用各种形状，如递减、递增、恒定、J 形和浴缸形。此外，所提出的系列不仅充分捕获了倾斜和对称的数据集，但它也可以更好地拟合均匀、过度、欠分散的数据。在生成一般类之后，广泛研究了 DOW-G 家族的两个特定分布。所提出的族的参数估计，通过最大似然法和贝叶斯方法进行了探索。进行紧凑的蒙特卡罗模拟研究以评估估计方法的行为。最后，我们通过使用两个不同的真实数据集解释了所提出的族的有用性。进行紧凑的蒙特卡罗模拟研究以评估估计方法的行为。最后，我们通过使用两个不同的真实数据集解释了所提出的族的有用性。进行紧凑的蒙特卡罗模拟研究以评估估计方法的行为。最后，我们通过使用两个不同的真实数据集解释了所提出的族的有用性。