具有危险功率参数的广义Gompertz分布及其二元推广:性质与应用

Q1 Decision Sciences Annals of Data Science Pub Date : 2022-08-01 DOI:10.1007/s40745-022-00420-w
Hiba Zeyada Muhammed
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引用次数: 0

摘要

最近,有一类新的分布被引入,命名为双变量危险幂参数分布族。本文介绍了一种广义的贡珀茨分布,它既是单变量分布,也是双变量分布。本文讨论了矩和矩生函数的不同性质。据观察,联合概率密度函数和联合生存函数可以用明确的形式表示。对模型未知参数考虑了最大似然估计。评估了未知参数的渐近置信区间。为了了解 MLE 的性能,还进行了一些模拟。为说明起见,将三个真实数据集应用于该模型。
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A Generalized Gompertz Distribution with Hazard Power Parameter and Its Bivariate Extension: Properties and Applications

Recently, a new class of distributions, named bivariate hazard power parameter family of distributions is introduced. In this paper, a generalized Gompertz distribution is introduced as a member of this family in both univariate and bivariate cases. Different properties are discussed as moments and moment generating function. It is observed that the joint probability density function and the joint survival function can be expressed in explicit forms. Maximum likelihood estimation is considered for the model unknown parameters. Asymptotic confidence intervals for the unknown parameters are evaluated. Some simulations have been performed to see the performances of the MLEs. Three real data sets are applied to this model for illustrative purposes.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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