{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T23:12:19Z","timestamp":1680390739743},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper develops some\nnon-parametric simultaneous confidence bands for survival function\nwhen data are randomly censored on the right. To construct the\nconfidence bands, a computer-assisted method is utilized and this\napproach requires no distributional assumptions, so the confidence\nbands can be easily estimated. The procedures are based on the\nintegrated martingale whose distribution is approximated by a\nGaussian process. The supremum distribution of the Gaussian process\ngenerated by simulation techniques leads to the construction of the\nconfidence bands. To improve the estimation procedures for the\nfinite sample sizes, the log-minus-log transformation is employed.\nThe proposed confidence bands are assessed using numerical\nsimulations and applied to a real-world data set regarding\nleukemia.<\/jats:p>","DOI":"10.1515\/mcma-2014-0003","type":"journal-article","created":{"date-parts":[[2014,12,2]],"date-time":"2014-12-02T18:16:29Z","timestamp":1417544189000},"page":"237-243","source":"Crossref","is-referenced-by-count":0,"title":["A martingale approach to estimating confidence\nband with censored data"],"prefix":"10.1515","volume":"20","author":[{"given":"Seung-Hwan","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Mathematics, Illinois Wesleyan University, Bloomington, Illinois 61701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eun-Joo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Millikin University, Decatur, Illinois 62522, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2014,9,13]]},"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2014-0003\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2014-0003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T22:35:42Z","timestamp":1680388542000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2014-0003\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,13]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2014,9,13]]},"published-print":{"date-parts":[[2014,12,1]]}},"alternative-id":["10.1515\/mcma-2014-0003"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2014-0003","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"value":"0929-9629","type":"print"},{"value":"1569-3961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9,13]]}}}