具有回归依赖结构的多维风险模型的精确大偏差

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Yang Liu, Ke-Ang Fu, Zhenlong Chen
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引用次数: 0

摘要

本文考虑一个非标准多维风险模型,其中索赔规模$\{\vec{X}_k, k\ge 1\}$是一个独立的、具有相关分量的同分布随机向量序列。通过假设到达间隔时间与索赔规模向量之间存在回归依赖结构,将回归依赖关系扩展到更实际的多维风险模型。对于尾部连续变化的索赔向量的单变量边际分布,我们得到了具有回归规模依赖结构的多维风险模型的精确大偏差公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Precise large deviations for a multidimensional risk model with regression dependence structure
In this paper, we consider a nonstandard multidimensional risk model, in which the claim sizes $\{\vec{X}_k, k\ge 1\}$ form an independent and identically distributed random vector sequence with dependent components. By assuming that there exists the regression dependence structure between inter-arrival time and the claim-size vectors, we extend the regression dependence to a more practical multidimensional risk model. For the univariate marginal distributions of claim vectors with consistently varying tails, we obtain the precise large deviation formulas for the multidimensional risk model with the regression size-dependent structure.
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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