{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T15:10:02Z","timestamp":1713712202097},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2012,3,16]],"date-time":"2012-03-16T00:00:00Z","timestamp":1331856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,6,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>The paper investigates the estimation problem in a regression-type model. To be able to deal with potential high dimensions, we provide a procedure called LOL\u2014for learning out of leaders\u2014with no optimization step. LOL is an autodriven algorithm with two thresholding steps. A first adaptive thresholding helps to select leaders among the initial regressors to obtain a first reduction of dimensionality. Then a second thresholding is performed on the linear regression on the leaders. The consistency of the procedure is investigated. Exponential bounds are obtained, leading to minimax and adaptive results for a wide class of sparse parameters, with (quasi) no restriction on the number p of possible regressors. An extensive computational experiment is conducted to emphasize the practical good performances of LOL.<\/jats:p>","DOI":"10.1111\/j.1467-9868.2011.01024.x","type":"journal-article","created":{"date-parts":[[2012,3,16]],"date-time":"2012-03-16T19:14:39Z","timestamp":1331925279000},"page":"475-513","source":"Crossref","is-referenced-by-count":4,"title":["Learning Out of Leaders"],"prefix":"10.1093","volume":"74","author":[{"given":"Mathilde","family":"Mougeot","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Diderot , Paris , France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominique","family":"Picard","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Diderot , Paris , France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karine","family":"Tribouley","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Ouest , Nanterre , France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2012,3,16]]},"reference":[{"key":"2023031108552246500_","volume-title":"Transductive versions of the lasso and the dantzig selector","author":"Alquier","year":"2009"},{"key":"2023031108552246500_","first-page":"229","article-title":"On the asymptotic efficiency of tests and estimates","volume":"22","author":"Bahadur","year":"1960","journal-title":"Sankhya"},{"key":"2023031108552246500_","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1214\/009053607000000631","article-title":"Approximation and learning by greedy algorithms","volume":"36","author":"Barron","year":"2008","journal-title":"Ann. 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