{"title":"时间非齐次自回归的几何递归","authors":"V. Golomoziy","doi":"10.15559/23-vmsta228","DOIUrl":null,"url":null,"abstract":"The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form Xn+1=αnXn+εn, where αn are constants, and εn are independent random variables. Conditions on αn and distributions of εn are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes X(1) and X(2) assuming that both αn(i), i∈{1,2}, and distributions of εn(i), i∈{1,2}, are close enough.","PeriodicalId":42685,"journal":{"name":"Modern Stochastics-Theory and Applications","volume":"44 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On geometric recurrence for time-inhomogeneous autoregression\",\"authors\":\"V. Golomoziy\",\"doi\":\"10.15559/23-vmsta228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form Xn+1=αnXn+εn, where αn are constants, and εn are independent random variables. Conditions on αn and distributions of εn are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes X(1) and X(2) assuming that both αn(i), i∈{1,2}, and distributions of εn(i), i∈{1,2}, are close enough.\",\"PeriodicalId\":42685,\"journal\":{\"name\":\"Modern Stochastics-Theory and Applications\",\"volume\":\"44 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Modern Stochastics-Theory and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15559/23-vmsta228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern Stochastics-Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15559/23-vmsta228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
On geometric recurrence for time-inhomogeneous autoregression
The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form Xn+1=αnXn+εn, where αn are constants, and εn are independent random variables. Conditions on αn and distributions of εn are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes X(1) and X(2) assuming that both αn(i), i∈{1,2}, and distributions of εn(i), i∈{1,2}, are close enough.