Modi-Weibull Distribution: Inferential and Simulation Study

Q1 Decision Sciences Annals of Data Science Pub Date : 2023-08-15 DOI:10.1007/s40745-023-00491-3
Harshita Kumawat, Kanak Modi, Pankaj Nagar
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Abstract

This paper presents a study on a new family of distributions using the Weibull distribution and termed as Modi-Weibull distribution. This Modi-Weibull distribution is based on four parameters. To understand the behaviour of the distribution, some statistical characteristics have been derived, such as shapes of density and distribution function, hazard function, survival function, median, moments, order statistics etc. These parameters are estimated using classical maximum likelihood estimation method. Asymptotic confidence intervals for parameters of Modi-Weibull distribution are also obtained. A simulation study is carried out to investigate the bias, MSE of proposed maximum likelihood estimators along with coverage probability and average width of confidence intervals of parameters. Two applications to real data sets are discussed to illustrate the fitting of the proposed distribution and compared with some well-known distributions.

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莫迪-威布尔分布:推理与仿真研究
本文研究了一种使用威布尔分布的新分布族,称为莫迪-威布尔分布。莫迪-韦布尔分布基于四个参数。为了解该分布的行为,我们推导出了一些统计特征,如密度和分布函数的形状、危险函数、生存函数、中位数、矩、阶次统计等。这些参数采用经典的最大似然估计法进行估计。此外,还获得了莫迪-韦布尔分布参数的渐近置信区间。我们还进行了模拟研究,以调查所提出的最大似然估计方法的偏差、MSE 以及参数置信区间的覆盖概率和平均宽度。讨论了两个真实数据集的应用,以说明拟议分布的拟合情况,并与一些著名的分布进行了比较。
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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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