{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:40:42Z","timestamp":1791891642173,"version":"4.3.4"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2002,10,1]],"date-time":"2002-10-01T00:00:00Z","timestamp":1033430400000},"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":[[2002,10,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>The paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.<\/jats:p>","DOI":"10.1111\/1467-9868.00361","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T19:37:40Z","timestamp":1047497860000},"page":"791-804","source":"Crossref","is-referenced-by-count":16,"title":["Bandwidth Selection for Local Linear Regression Smoothers"],"prefix":"10.1093","volume":"64","author":[{"given":"Nicolas W.","family":"Hengartner","sequence":"first","affiliation":[{"name":"Yale University , New Haven , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marten H.","family":"Wegkamp","sequence":"additional","affiliation":[{"name":"Yale University , New Haven , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Matzner-L\u00f8ber","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Rennes II and Centre de Recherche en Economie et Statistique\u2013Ecole Nationale de la Statistique et de l'Analyse de l'Information , Rennes , France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2002,10,23]]},"reference":[{"key":"2023040109513037400_","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s004400050210","article-title":"Risk bounds for model selection via penalization","volume":"113","author":"Barron","year":"1999","journal-title":"Probab. 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