{"title":"关于AR(1)协方差的特征结构","authors":"P. Sherman","doi":"10.1109/SSP53291.2023.10208005","DOIUrl":null,"url":null,"abstract":"In this work we first review and elaborate on the eigenstructure of the covariance matrix for an autoregressive process of order 1. We then address the statistical elements related to its estimator in relation to the maximum eigenvalue. Bias, uncertainty, and distributions are provided in relation to the estimators of the various parameters associated with both the eigenvalue and eigenvector.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Eigenstructure of the AR(1) Covariance\",\"authors\":\"P. Sherman\",\"doi\":\"10.1109/SSP53291.2023.10208005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we first review and elaborate on the eigenstructure of the covariance matrix for an autoregressive process of order 1. We then address the statistical elements related to its estimator in relation to the maximum eigenvalue. Bias, uncertainty, and distributions are provided in relation to the estimators of the various parameters associated with both the eigenvalue and eigenvector.\",\"PeriodicalId\":296346,\"journal\":{\"name\":\"2023 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP53291.2023.10208005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP53291.2023.10208005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this work we first review and elaborate on the eigenstructure of the covariance matrix for an autoregressive process of order 1. We then address the statistical elements related to its estimator in relation to the maximum eigenvalue. Bias, uncertainty, and distributions are provided in relation to the estimators of the various parameters associated with both the eigenvalue and eigenvector.
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