{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:05:23Z","timestamp":1791263123575,"version":"4.1.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":2019,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000275","name":"Leverhulme Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000275","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012338","name":"Alan Turing Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Change points are a very common feature of \u2018big data\u2019 that arrive in the form of a data stream. We study high dimensional time series in which, at certain time points, the mean structure changes in a sparse subset of the co-ordinates. The challenge is to borrow strength across the co-ordinates to detect smaller changes than could be observed in any individual component series. We propose a two-stage procedure called inspect for estimation of the change points: first, we argue that a good projection direction can be obtained as the leading left singular vector of the matrix that solves a convex optimization problem derived from the cumulative sum transformation of the time series. We then apply an existing univariate change point estimation algorithm to the projected series. Our theory provides strong guarantees on both the number of estimated change points and the rates of convergence of their locations, and our numerical studies validate its highly competitive empirical performance for a wide range of data-generating mechanisms. Software implementing the methodology is available in the R package InspectChangepoint.<\/jats:p>","DOI":"10.1111\/rssb.12243","type":"journal-article","created":{"date-parts":[[2017,8,12]],"date-time":"2017-08-12T00:22:40Z","timestamp":1502497360000},"page":"57-83","source":"Crossref","is-referenced-by-count":172,"title":["High Dimensional Change Point Estimation via Sparse Projection"],"prefix":"10.1093","volume":"80","author":[{"given":"Tengyao","family":"Wang","sequence":"first","affiliation":[{"name":"University of Cambridge \u00a0 UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard J.","family":"Samworth","sequence":"additional","affiliation":[{"name":"University of Cambridge \u00a0 UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,8,11]]},"reference":[{"key":"2023022005314389900_","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1137\/050645506","article-title":"A direct formulation for sparse PCA using semidefinite programming","volume":"49","author":"d\u2019Aspremont","year":"2007","journal-title":"SIAM Rev."},{"key":"2023022005314389900_","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1214\/12-AOAS565","article-title":"Evaluating stationarity via change-point alternatives with applications to fMRI data","volume":"6","author":"Aston","year":"2012","journal-title":"Ann. 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