{"title":"自相关的二次-逆估计","authors":"D. Thomson","doi":"10.1109/SSP.2018.8450755","DOIUrl":null,"url":null,"abstract":"We reconsider the classical problem of estimating the auto-correlation sequence of a stationary time series using quadratic-inverse spectrum estimates. This paper collapses the free-parameter expansion ambiguity of quadratic-inverse spectrum estimates and results in estimates of autocorrelations that have simultaneously low bias and variance.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quadratic–Inverse Estimates Of Autocorrelation\",\"authors\":\"D. Thomson\",\"doi\":\"10.1109/SSP.2018.8450755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We reconsider the classical problem of estimating the auto-correlation sequence of a stationary time series using quadratic-inverse spectrum estimates. This paper collapses the free-parameter expansion ambiguity of quadratic-inverse spectrum estimates and results in estimates of autocorrelations that have simultaneously low bias and variance.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"149 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450755\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450755","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We reconsider the classical problem of estimating the auto-correlation sequence of a stationary time series using quadratic-inverse spectrum estimates. This paper collapses the free-parameter expansion ambiguity of quadratic-inverse spectrum estimates and results in estimates of autocorrelations that have simultaneously low bias and variance.
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