{"title":"多季节离散时间风险模型再探讨","authors":"Andrius Grigutis, Jonas Jankauskas, Jonas Šiaulys","doi":"10.1007/s10986-023-09613-z","DOIUrl":null,"url":null,"abstract":"<p>In this work, we set up the distribution function of <span>\\(\\mathcal{M}:={\\mathrm{sup}}_{n\\ge 1}{\\sum }_{i=1}^{n}\\left({X}_{i}-1\\right),\\)</span> where the random walk <span>\\({\\sum }_{i=1}^{n}{X}_{i},n\\in {\\mathbb{N}},\\)</span> is generated by <i>N</i> periodically occurring distributions, and the integer-valued and nonnegative random variables<i>X</i><sub>1</sub><i>,X</i><sub>2</sub><i>, . . .</i> are independent. The considered random walk generates a so-called multiseasonal discrete-time risk model, and a known distribution of random variable <i>M</i> enables us to calculate the ultimate time ruin or survival probability. Verifying obtained theoretical statements, we demonstrate several computational examples for survival probability <b>P</b>(<i>M < u</i>) when <i>N</i> = 2<i>,</i> 3<i>,</i> or 10.</p>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiseasonal discrete-time risk model revisited\",\"authors\":\"Andrius Grigutis, Jonas Jankauskas, Jonas Šiaulys\",\"doi\":\"10.1007/s10986-023-09613-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this work, we set up the distribution function of <span>\\\\(\\\\mathcal{M}:={\\\\mathrm{sup}}_{n\\\\ge 1}{\\\\sum }_{i=1}^{n}\\\\left({X}_{i}-1\\\\right),\\\\)</span> where the random walk <span>\\\\({\\\\sum }_{i=1}^{n}{X}_{i},n\\\\in {\\\\mathbb{N}},\\\\)</span> is generated by <i>N</i> periodically occurring distributions, and the integer-valued and nonnegative random variables<i>X</i><sub>1</sub><i>,X</i><sub>2</sub><i>, . . .</i> are independent. The considered random walk generates a so-called multiseasonal discrete-time risk model, and a known distribution of random variable <i>M</i> enables us to calculate the ultimate time ruin or survival probability. Verifying obtained theoretical statements, we demonstrate several computational examples for survival probability <b>P</b>(<i>M < u</i>) when <i>N</i> = 2<i>,</i> 3<i>,</i> or 10.</p>\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2023-12-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s10986-023-09613-z\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10986-023-09613-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在这项工作中,我们设定了分布函数({M}:={\mathrm{sup}}_{n\ge 1}{\sum }_{i=1}^{n}\left({X}_{i}-1\right),\) 其中随机行走 \({\sum }_{i=1}^{n}{X}_{i},n\in {\mathbb{N}},\) 是由 N 个周期性出现的分布生成的,且整数值和非负随机变量 X1,X2, ....是独立的。所考虑的随机漫步生成了一个所谓的多季节离散时间风险模型,已知随机变量 M 的分布使我们能够计算最终时间毁灭或生存概率。为了验证所获得的理论陈述,我们演示了几个计算实例,说明当 N = 2、3 或 10 时的生存概率 P(M < u)。
In this work, we set up the distribution function of \(\mathcal{M}:={\mathrm{sup}}_{n\ge 1}{\sum }_{i=1}^{n}\left({X}_{i}-1\right),\) where the random walk \({\sum }_{i=1}^{n}{X}_{i},n\in {\mathbb{N}},\) is generated by N periodically occurring distributions, and the integer-valued and nonnegative random variablesX1,X2, . . . are independent. The considered random walk generates a so-called multiseasonal discrete-time risk model, and a known distribution of random variable M enables us to calculate the ultimate time ruin or survival probability. Verifying obtained theoretical statements, we demonstrate several computational examples for survival probability P(M < u) when N = 2, 3, or 10.