arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2408.01010math.PR
收藏

广义拟渐近独立下随机加权和的联合尾部

Joint tail of randomly weighted sums under generalized quasi asymptotic independence

Dimitrios G. Konstantinides, Charalampos D. Passalidis

AI总结:

本文在广义拟渐近独立下研究二维随机加权和的联合尾部,提出新依赖结构与二维重尾类结合的方法,并给出有限时间破产概率。

摘要:

本文重新审视了二维随机加权和的最大-和等价这一经典问题。与文献中大多数论文相反,我们考虑主随机变量之间存在某种相互依赖关系,这是通过将一种新的依赖结构与某些二维重尾分布类相结合来实现的。此外,我们引入了一种二维正则变化分布的新方法,与公认的多元正则变化分布相比,该方法与多元非线性单大跳跃原理一致。我们研究了此类及其他二维类的封闭性质。我们的结果包含二维离散时间风险模型中的有限时间破产概率。

英文摘要:

In this paper we revisited the classical problem of max-sum equivalence of randomly weighted sums in two dimensions. In opposite to the most papers in literature, we consider that there exists some interdependence between the primary random variables, which is achieved by a combination of a new dependence structure with some two-dimensional heavy-tailed classes of distributions. Further, we introduce a new approach in two-dimensional regular varying distributions, that in contrast to well-established multivariate regularly varying distributions, is consistent with the multivariate non-linear single big jump principle. We study some closure properties of this, and of other two-dimensional classes. Our results contain the finite-time ruin probability in a two-dimensional discrete time risk model

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑