{"title":"光谱和交叉光谱的自举置信带","authors":"D. Politis, Joseph P. Romano, T. Lai","doi":"10.1109/MDSP.1989.97045","DOIUrl":null,"url":null,"abstract":"Summary form only given. Nonparametric bootstrap confidence intervals and bands have been constructed from kernel and lag-window spectral estimators. The results can be of use in a finite sample situation, especially when it cannot be assumed that the time series is Gaussian. Monte Carlo simulations have been carried out in order to compare the bootstrap confidence bands with the asymptotic ones.<<ETX>>","PeriodicalId":340681,"journal":{"name":"Sixth Multidimensional Signal Processing Workshop,","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":"{\"title\":\"Bootstrap confidence bands for spectra and cross-spectra\",\"authors\":\"D. Politis, Joseph P. Romano, T. Lai\",\"doi\":\"10.1109/MDSP.1989.97045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. Nonparametric bootstrap confidence intervals and bands have been constructed from kernel and lag-window spectral estimators. The results can be of use in a finite sample situation, especially when it cannot be assumed that the time series is Gaussian. Monte Carlo simulations have been carried out in order to compare the bootstrap confidence bands with the asymptotic ones.<<ETX>>\",\"PeriodicalId\":340681,\"journal\":{\"name\":\"Sixth Multidimensional Signal Processing Workshop,\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"44\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth Multidimensional Signal Processing Workshop,\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MDSP.1989.97045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth Multidimensional Signal Processing Workshop,","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDSP.1989.97045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bootstrap confidence bands for spectra and cross-spectra
Summary form only given. Nonparametric bootstrap confidence intervals and bands have been constructed from kernel and lag-window spectral estimators. The results can be of use in a finite sample situation, especially when it cannot be assumed that the time series is Gaussian. Monte Carlo simulations have been carried out in order to compare the bootstrap confidence bands with the asymptotic ones.<>