变换的移位林德利分布:特征、经典和贝叶斯估计及其应用

Q1 Decision Sciences
A. Chakraborty, S. Rana, S. I. Maiti
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引用次数: 0

摘要

为了进一步改进现有的林德利分布,本文提出了移位林德利分布的二次秩变换映射方法。加入一个额外的偏度参数\(\lambda \)来改变分布。由此引入的分布称为变形移位林德利分布。我们提供了该分布的统计特性及其可靠性行为的全面描述。给出了相关参数的热图。在估计部分,讨论了参数的极大似然估计和贝叶斯估计。进行了详细的仿真研究。最后,一个实际数据应用说明了拟合所提出分布的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transmuted Shifted Lindley Distribution: Characterizations, Classical and Bayesian Estimation with Applications

Transmuted Shifted Lindley Distribution: Characterizations, Classical and Bayesian Estimation with Applications

In this article, we propose the quadratic rank transmutation map approach on shifted Lindley distribution to improve the existing distribution further. An additional skewness parameter \(\lambda \) is incorporated to transmute the distribution. The distribution, hence introduced, is called the Transmuted Shifted Lindley distribution. We provide a comprehensive description of this distribution’s statistical properties and its reliability behavior. The heat maps on the associated parameters are presented. In the estimation section, both maximum likelihood and Bayesian estimation of parameters are discussed. A detailed simulation study is performed. Finally, a real data application illustrates the performance of fitting to the proposed distribution.

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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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