Analyzing Insurance Data with an Alpha Power Transformed Exponential Poisson Model

Q1 Decision Sciences
Mohammed A. Meraou, Mohammad Z. Raqab, Fatmah B. Almathkour
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引用次数: 0

Abstract

In this paper, we propose a new model by adding an additional parameter to the baseline distributions for modeling claim and risk data used in actuarial and financial studies. The new model is called alpha power transformed exponential Poisson model. It has three parameters and its probability density function can be skewed and unimodal. Several distributional properties of the proposed model such as reliability, hazard rate, quantile and moments are established. Estimation of the unknown parameters based on maximum likelihood estimation are derived and risk measures such as value at risk and tail value at risk are computed. Moreover, the performance of these measures is illustrated via numerical simulation experiments. Finally, two real data sets of insurance losses are analyzed to check the potential of the proposed model among some of the existing models.

用阿尔法幂变换指数泊松模型分析保险数据
在本文中,我们提出了一个新的模型,通过在基线分布中添加一个额外的参数来建模精算和金融研究中使用的索赔和风险数据。该模型称为幂变换指数泊松模型。它有三个参数,其概率密度函数可以是偏态和单峰的。建立了该模型的可靠性、风险率、分位数和矩等分布特性。导出了基于极大似然估计的未知参数估计,并计算了风险值和风险尾值等风险度量。并通过数值模拟实验验证了这些措施的有效性。最后,通过对两个真实的保险损失数据集的分析,验证了该模型在现有模型中的潜力。
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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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