A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data

Q1 Decision Sciences Annals of Data Science Pub Date : 2023-08-08 DOI:10.1007/s40745-023-00485-1
Majid Hashempour, Morad Alizadeh, Haitham M. Yousof
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Abstract

The objectives of this study are to propose a new two-parameter lifespan distribution and explain some of the most essential properties of that distribution. Through the course of this investigation, we will be able to achieve both of these objectives. For the aim of assessment, research is carried out that makes use of simulation, and for the same reason, a variety of various approaches are studied and taken into account for the purpose of evaluation. Making use of two separate data collections enables an analysis of the adaptability of the suggested distribution to a number of different contexts. The risk exposure in the context of asymmetric bimodal right-skewed precipitation data was further defined by using five essential risk indicators, such as value-at-risk, tail-value-at-risk, tail variance, tail mean–variance, and mean excess loss function. This was done in order to account for the right-skewed distribution of the data. In order to examine the data, several risk indicators were utilized. These risk indicators were used in order to achieve a more in-depth description of the risk exposure that was being faced.

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一个新的Lindley推广:双峰右偏降水数据下的估算、风险评估与分析
本研究的目标是提出一种新的双参数寿命分布,并解释该分布的一些最基本特性。通过这项研究,我们将能够实现这两个目标。为了评估的目的,我们开展了利用模拟的研究,出于同样的原因,我们研究并考虑了各种不同的评估方法。利用两种不同的数据收集方式,可以分析建议的分配方式在不同情况下的适应性。在非对称双峰右斜降水数据的背景下,通过使用五个基本风险指标,如风险值、尾部风险值、尾部方差、尾部平均方差和平均超额损失函数,进一步定义了风险暴露。这样做是为了考虑数据的右偏分布。为了检验数据,使用了几个风险指标。使用这些风险指标是为了更深入地描述所面临的风险。
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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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