Marshall-Olkin-Gompertz分布:性质与应用

IF 1.6 Q1 STATISTICS & PROBABILITY
J. T. Eghwerido, Joel Oruaoghene Ogbo, Adebola Evelyn Omotoye
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引用次数: 2

摘要

本文介绍了Marshall-Olkin变换族中寿命泊松过程的三个参数类,即递增、浴缸和偏斜。导出了Marshall-Olkin-Gompertz(MO-G)模型的一些结构数学性质。采用最大似然法建立了MO-G模型参数。通过仿真检验了MO-G模型估计器的灵活性、效率和行为。MO-G模型的经验适用性、灵活性和熟练程度通过真实数据集进行了仔细检查。与统计文献中的现有模型相比,所提出的MO-G模型提供了更好的拟合,并且可以作为泊松过程建模中出现的模型的替代模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Marshall-Olkin Gompertz Distribution: Properties and Applications
This article introduces three parameters class for lifetime Poisson processes in the Marshall-Olkin transformation family that are increasing, bathtub and skewed. Some structural mathematical properties of the Marshall-Olkin Gompertz (MO-G) model were derived. The MO-G model parameters were established by maximum likelihood approach. The flexibility, efficiency, and behavior of the MO-G model estimators were examined through simulation. The empirical applicability, flexibility and proficiency of the MO-G model was scrutinized by a real-life dataset. The proposed MO-G model provides a better fit when compared to existing models in statistical literature and can serve as an alternative model to those appearing in modeling Poisson processes.
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
自引率
0.00%
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0
审稿时长
10 weeks
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