{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T14:57:49Z","timestamp":1791125869432,"version":"4.1.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,15]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Bootstrapping spectral mean statistics has been a notoriously difficult problem over the past 25 years. Many frequency domain bootstraps are valid only for certain time series structures, e.g., linear processes, or for special types of statistics, i.e., ratio statistics, because such bootstraps fail to capture the limiting variance of spectral statistics in general settings. We address this issue with a different form of resampling, namely, subsampling. While not considered previously, subsampling provides consistent variance estimation under much weaker conditions than any existing bootstrap in the frequency domain. Mixing is not used, as is often standard with subsampling. Rather, subsampling can be generally justified under the same conditions needed for original spectral mean statistics to have distributional limits in the first place. This result has impacts for other bootstrap methods. Subsampling then applies to extending the validity of recent state-of-the-art bootstraps in the frequency domain. We nontrivially link subsampling to such bootstraps, which broadens their range, as moment and block assumptions needed for these are cut by more than half. Essentially, state-of-the-art bootstraps then require no more stringent assumptions than those needed for a target limit distribution to exist, which is unusual in the bootstrap world. We also close a gap in the theory of subsampling for time series with distributional approximations, in addition to variance estimation, for frequency domain statistics.<\/jats:p>","DOI":"10.1093\/biomet\/asad006","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T21:22:23Z","timestamp":1675113743000},"page":"1099-1115","source":"Crossref","is-referenced-by-count":8,"title":["A subsampling perspective for extending the validity of state-of-the-art bootstraps in the frequency domain"],"prefix":"10.1093","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8230-1311","authenticated-orcid":false,"given":"Haihan","family":"Yu","sequence":"first","affiliation":[{"name":"Iowa State University Department of Statistics, , 2438 Osborn Drive, Ames, Iowa 50011, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark S","family":"Kaiser","sequence":"additional","affiliation":[{"name":"Iowa State University Department of Statistics, , 2438 Osborn Drive, Ames, Iowa 50011, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel J","family":"Nordman","sequence":"additional","affiliation":[{"name":"Iowa State University Department of Statistics, , 2438 Osborn Drive, Ames, Iowa 50011, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"2023111615233212900_B1","doi-asserted-by":"crossref","DOI":"10.1137\/1.9780898719246","volume-title":"Time Series: Data Analysis and Theory","author":"Brillinger,","year":"2001"},{"key":"2023111615233212900_B2","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/0047-259X(85)90028-4","article-title":"Asymptotic normality of spectral estimates","volume":"16","author":"Dahlhaus,","year":"1985","journal-title":"J. Mult. Anal."},{"key":"2023111615233212900_B3","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1214\/aos\/1069362304","article-title":"A frequency domain bootstrap for ratio statistics in time series analysis","volume":"24","author":"Dahlhaus,","year":"1996","journal-title":"Ann. Statist."},{"key":"2023111615233212900_B4","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-69952-3","volume-title":"Weak Dependence: With Examples and Applications","author":"Dedecker,","year":"2007"},{"key":"2023111615233212900_B5","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1214\/06-PS086","article-title":"The notion of $\\psi$-weak dependence and its applications to bootstrapping time series","volume":"5","author":"Doukhan,","year":"2008","journal-title":"Prob. 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Am. Statist. Assoc."},{"key":"2023111615233212900_B10","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asac044","article-title":"Bootstrapping Whittle estimators","author":"Kreiss,","year":"2022","journal-title":"Biometrika"},{"key":"2023111615233212900_B11","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1214\/11-AOS900","article-title":"On the range of validity of the autoregressive sieve bootstrap","volume":"39","author":"Kreiss,","year":"2011","journal-title":"Ann. Statist."},{"key":"2023111615233212900_B12","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3803-2","volume-title":"Resampling Methods for Dependent Data","author":"Lahiri,","year":"2003"},{"key":"2023111615233212900_B13","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1214\/19-AOS1892","article-title":"Extending the validity of frequency domain bootstrap methods to general stationary processes","volume":"48","author":"Meyer,","year":"2020","journal-title":"Ann. Statist."},{"key":"2023111615233212900_B14","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1214\/009053606000000902","article-title":"A frequency domain empirical likelihood for short- and long-range dependence","volume":"34","author":"Nordman,","year":"2006","journal-title":"Ann. Statist."},{"key":"2023111615233212900_B15","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1214\/aos\/1176325770","article-title":"Large sample confidence regions based on subsamples under minimal assumptions","volume":"22","author":"Politis,","year":"1994","journal-title":"Ann. 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Prob."}],"container-title":["Biometrika"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asad006\/51402039\/asad006.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/article-pdf\/110\/4\/1099\/53471780\/asad006.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/article-pdf\/110\/4\/1099\/53471780\/asad006.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T16:33:40Z","timestamp":1700152420000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biomet\/article\/110\/4\/1099\/7009228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":19,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,1,30]]},"published-print":{"date-parts":[[2023,11,15]]}},"URL":"https:\/\/doi.org\/10.1093\/biomet\/asad006","relation":{},"ISSN":["0006-3444","1464-3510"],"issn-type":[{"value":"0006-3444","type":"print"},{"value":"1464-3510","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,12,1]]},"published":{"date-parts":[[2023,1,30]]}}}