凸组合修正Lindley分布及其工程应用

Q1 Decision Sciences
Afaq Ahmad, A. A. Bhat, S. P. Ahmad, Raheela Jan
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引用次数: 0

摘要

利用指数分布和伽玛分布的凸组合,提出了一种改进的林德利分布。推导了该分布的基本性质,如分布的形状、矩、均值、方差、可靠性、危险率、矩生成函数、随机排序和有序统计量的分布。所提出的分布被观察到是一个重尾分布,也可以用来模拟具有倒浴缸形状的数据,因为它的危险率函数。得到了所提分布的未知参数的极大似然估计。给出了两个数值例子来证明所提出的分布的适用性,并且对于两个实际数据集,所提出的分布在充分模拟重尾数据方面优于许多其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Modified Lindley Distribution Through Convex Combination with Applications in Engineering

This paper introduces a Modified Lindley distribution using a convex combination of exponential and gamma distribution. The fundamental properties of the proposed distribution such as the shapes of the distribution, moments, mean, variance, reliability, hazard rate, moment generating function, stochastic ordering and the distribution of order statistics have been derived. The proposed distribution is observed to be a heavy-tailed distribution and can also be used to model data with upside-down bathtub shape for its hazard rate function. The maximum likelihood estimators of the unknown parameters of the proposed distribution have been obtained. Two numerical examples are given to demonstrate the applicability of the proposed distribution and for the two real data sets, the proposed distribution is found to be superior in its ability to sufficiently model heavy-tailed data than many other models.

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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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