{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T21:47:04Z","timestamp":1793137624941,"version":"4.5.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"European Research Council","doi-asserted-by":"publisher","award":["786461"],"award-info":[{"award-number":["786461"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DFG Cluster of Excellence Multiscale Bioimaging","award":["EXC 2067"],"award-info":[{"award-number":["EXC 2067"]}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["5381"],"award-info":[{"award-number":["5381"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,11]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We propose seeded binary segmentation for large-scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of changepoints based on these candidates can be done in various ways, adapted to the problem at hand. The method is thus easy to adapt to many changepoint problems, ranging from univariate to high dimensional. Compared to recently popular random background intervals, seeded intervals lead to reproducibility and much faster computations. For the univariate Gaussian change in mean set-up, the methodology is shown to be asymptotically minimax optimal when paired with appropriate selection criteria. We demonstrate near-linear runtimes and competitive finite sample estimation performance. Furthermore, we illustrate the versatility of our method in high-dimensional settings.<\/jats:p>","DOI":"10.1093\/biomet\/asac052","type":"journal-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T20:32:02Z","timestamp":1664569922000},"page":"249-256","source":"Crossref","is-referenced-by-count":56,"title":["Seeded binary segmentation: a general methodology for fast and optimal changepoint detection"],"prefix":"10.1093","volume":"110","author":[{"given":"S","family":"Kov\u00e1cs","sequence":"first","affiliation":[{"name":"Department of Mathematics Seminar for Statistics, , ETH Zurich, R\u00e4mistrasse 101, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P","family":"B\u00fchlmann","sequence":"additional","affiliation":[{"name":"Department of Mathematics Seminar for Statistics, , ETH Zurich, R\u00e4mistrasse 101, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H","family":"Li","sequence":"additional","affiliation":[{"name":"University of G\u00f6ttingen Institute for Mathematical Stochastics, , Goldschmidtstrasse 7, 37077 G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Munk","sequence":"additional","affiliation":[{"name":"University of G\u00f6ttingen Institute for Mathematical Stochastics, , Goldschmidtstrasse 7, 37077 G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,10,3]]},"reference":[{"key":"2023021111212510100_B1","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1111\/rssb.12322","article-title":"Narrowest-over-threshold detection of multiple change points and change-point-like features","volume":"81","author":"Baranowski,","year":"2019","journal-title":"J. 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