{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T20:16:05Z","timestamp":1792095365809,"version":"4.4.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T00:00:00Z","timestamp":1372809600000},"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":[[2014,1,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We study distribution-free, non-parametric prediction bands with a focus on their finite sample behaviour. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band by combining the idea of \u2018conformal prediction\u2019 with non-parametric conditional density estimation. The proposed estimator, called COPS (conformal optimized prediction set), always has a finite sample guarantee. Under regularity conditions the estimator converges to an oracle band at a minimax optimal rate. A fast approximation algorithm and a data-driven method for selecting the bandwidth are developed. The method is illustrated in simulated and real data examples.<\/jats:p>","DOI":"10.1111\/rssb.12021","type":"journal-article","created":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T04:12:41Z","timestamp":1372824761000},"page":"71-96","source":"Crossref","is-referenced-by-count":205,"title":["Distribution-free Prediction Bands for Non-parametric Regression"],"prefix":"10.1093","volume":"76","author":[{"given":"Jing","family":"Lei","sequence":"first","affiliation":[{"name":"Carnegie Mellon University , Pittsburgh , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larry","family":"Wasserman","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University , Pittsburgh , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,7,3]]},"reference":[{"key":"2023031306053047200_","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/1467-9469.00254","article-title":"Non-parametric estimation of the residual distribution","volume":"28","author":"Akritas","year":"2001","journal-title":"Scand. 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