A New Regularly Varying Discrete Distribution Generated by Waring-Type Probability

Pub Date : 2024-04-25 DOI:10.3103/s106836232470002x
D. Farbod
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Abstract

In this paper, based on the discretization method, we construct a new 2-parameter regularly varying discrete distribution generated by Waring-type probability (2-RDWP). Some useful plots are displayed for the model. From the mathematical point of view, to suggest 2-RDWP as a new discrete probability distribution in bioinformatics, some statistical facts such as unimodality, skewness to the right, upward/downward convexity, regular variation at infinity and asymptotically constant slowly varying component are established for the model. We provide the conditions of coincidence of solution for the system of likelihood equations with the maximum likelihood estimators for the unknown parameters. Simulation studies are performed using the Monte Carlo method and Nelder–Mead optimization algorithm to obtain maximum likelihood estimations of the unknown parameters. Asymptotic expansion of the probability function with two terms is considered, and then the moment’s existence of integer orders is investigated. Finally, a real count data set is used to show the applicability of the new model compared to other models in bioinformatics.

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由瓦林型概率生成的一种新的正则变化离散分布
摘要 本文以离散化方法为基础,构建了一种新的由沃林型概率(2-RDWP)生成的双参数规律变化离散分布。文中展示了该模型的一些有用曲线图。从数学角度来看,为了将 2-RDWP 作为一种新的离散概率分布应用于生物信息学,我们为模型建立了一些统计事实,如单调性、向右偏斜、向上/向下凸性、无穷大时的规则变化和渐近恒定的缓慢变化分量。我们提供了似然方程组解与未知参数最大似然估计值重合的条件。使用蒙特卡罗方法和 Nelder-Mead 优化算法进行了模拟研究,以获得未知参数的最大似然估计值。考虑了有两个项的概率函数的渐近展开,然后研究了矩的整阶存在性。最后,使用一个真实的计数数据集来展示新模型与生物信息学中其他模型相比的适用性。
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