{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T11:13:28Z","timestamp":1771586008282,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Halicioglu Data Science Institute"},{"DOI":"10.13039\/100005595","name":"University of California","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Subsampling has seen a resurgence in the big data era where the standard, full-resample size bootstrap can be infeasible to compute. Nevertheless, even choosing a single random subsample of size b can be computationally challenging with both b and the sample size n being very large. This paper shows how a set of appropriately chosen, nonrandom subsamples can be used to conduct effective, and computationally feasible, subsampling distribution estimation. Furthermore, the same set of subsamples can be used to yield a procedure for subsampling aggregation, also known as subagging, that is scalable with big data. Interestingly, the scalable subagging estimator can be tuned to have the same, or better, rate of convergence than that of \u03b8^n. Statistical inference could then be based on the scalable subagging estimator instead of the original \u03b8^n.<\/jats:p>","DOI":"10.1093\/biomet\/asad021","type":"journal-article","created":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T06:50:01Z","timestamp":1679122201000},"page":"347-354","source":"Crossref","is-referenced-by-count":6,"title":["Scalable subsampling: computation, aggregation and inference"],"prefix":"10.1093","volume":"111","author":[{"given":"Dimitris N","family":"Politis","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of California , San Diego, 9500 Gilman Drive, La Jolla , California 92093-0112, U.S.A"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"2024032102140095100_asad021-B1","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1214\/17-AOS1633","article-title":"Divide and conquer in nonstandard problems and the super-efficiency phenomenon","volume":"47","author":"Banerjee","year":"2019","journal-title":"Ann. Statist."},{"key":"2024032102140095100_asad021-B2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1111\/sjos.12243","article-title":"Empirical processes in survey sampling with (conditional) Poisson designs","volume":"44","author":"Bertail","year":"2017","journal-title":"Scand. J. Statist"},{"key":"2024032102140095100_asad021-B3","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1214\/15-EJS1085","article-title":"Randomized maximum-contrast selection: subagging for large-scale regression","volume":"10","author":"Bradic","year":"2016","journal-title":"Electron. J. Statist."},{"key":"2024032102140095100_asad021-B4","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1214\/aos\/1031689014","article-title":"Analyzing bagging","volume":"30","author":"B\u00fchlmann","year":"2002","journal-title":"Ann. Statist."},{"key":"2024032102140095100_asad021-B5","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1214\/21-AOS2062","article-title":"Distributed statistical inference for massive data","volume":"49","author":"Chen","year":"2021","journal-title":"Ann. Statist."},{"key":"2024032102140095100_asad021-B6","first-page":"1655","article-title":"A split-and-conquer approach for analysis of extraordinarily large data","volume":"24","author":"Chen","year":"2014","journal-title":"Statist. Sinica"},{"key":"2024032102140095100_asad021-B7","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. Statist."},{"key":"2024032102140095100_asad021-B8","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.3150\/12-BEJSP17","article-title":"On statistics, computation and scalability","volume":"19","author":"Jordan","year":"2013","journal-title":"Bernoulli"},{"key":"2024032102140095100_asad021-B9","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1111\/rssb.12050","article-title":"A scalable bootstrap for massive data","volume":"76","author":"Kleiner","year":"2014","journal-title":"J. R. Statist. Soc. B"},{"key":"2024032102140095100_asad021-B10","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.csda.2009.08.009","article-title":"Fast surrogates of U-statistics","volume":"54","author":"Lin","year":"2010","journal-title":"Comp. Statist. Data Anal"},{"key":"2024032102140095100_asad021-B11","author":"Politis","year":"2021"},{"key":"2024032102140095100_asad021-B12","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. Statist."},{"key":"2024032102140095100_asad021-B13","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-1554-7","volume-title":"Subsampling","author":"Politis","year":"1999"},{"key":"2024032102140095100_asad021-B14","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1080\/01621459.2015.1080709","article-title":"A subsampled double bootstrap for massive data","volume":"111","author":"Sengupta","year":"2016","journal-title":"J. Am. Statist. Assoc"},{"key":"2024032102140095100_asad021-B15","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1214\/18-AOS1695","article-title":"Convolved subsampling estimation with applications to block bootstrap","volume":"47","author":"Tewes","year":"2019","journal-title":"Ann. Statist."},{"key":"2024032102140095100_asad021-B16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Statist. Soc. B"},{"key":"2024032102140095100_asad021-B17","author":"Ting","year":"2021"},{"key":"2024032102140095100_asad021-B18","first-page":"1","article-title":"A review on optimal subsampling methods for massive datasets","volume":"19","author":"Yao","year":"2021","journal-title":"J. Data Sci"},{"key":"2024032102140095100_asad021-B19","first-page":"3321","article-title":"Communication-efficient algorithms for statistical optimization","volume":"14","author":"Zhang","year":"2013","journal-title":"J. Mach. Learn. Res"},{"key":"2024032102140095100_asad021-B20","author":"Zou","year":"2021"}],"container-title":["Biometrika"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asad021\/50422703\/asad021.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/article-pdf\/111\/1\/347\/56665622\/asad021.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biomet\/article-pdf\/111\/1\/347\/56665622\/asad021.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T16:35:15Z","timestamp":1729096515000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biomet\/article\/111\/1\/347\/7082504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3,21]]},"published-print":{"date-parts":[[2024,2,12]]}},"URL":"https:\/\/doi.org\/10.1093\/biomet\/asad021","relation":{},"ISSN":["0006-3444","1464-3510"],"issn-type":[{"value":"0006-3444","type":"print"},{"value":"1464-3510","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2023,3,21]]}}}