Majid Hashempour, Morad Alizadeh, Haitham M. Yousof
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A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data
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.
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.