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Article: Ranked set sampling in the presence of censored data

TitleRanked set sampling in the presence of censored data
Authors
KeywordsJudgment ranking
Kaplan-Meier method
Left censoring
Maximum likelihood method
Ranked set sampling
Simple random sampling
Issue Date2002
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/6285
Citation
Environmetrics, 2002, v. 13 n. 4, p. 379-396 How to Cite?
AbstractThe ranked set sampling (RSS) technique has been shown to be superior to classical simple random sampling (SRS) in the sense that it always provides a more precise estimator of the population mean. However, it is quite often that some measurements are below the limit of detection and hence become censored. In such situations, the superiority of RSS over SRS may no longer be held. In this article we consider the problem of estimating the population mean and standard deviation based on a ranked set sample with some data being censored. Maximum likelihood estimators are proposed when the data are assumed to follow a lognormal distribution. In the case where the distribution is unknown, a variant of the Kaplan-Meier estimator is proposed in the estimation of the population mean. A simulation study is conducted to compare the performance of the proposed RSS estimators with the corresponding SRS estimators. The impact of imperfect judgment ranking is also discussed. The proposed methods are applied to a real data set on mercury concentration in swordfish. Copyright © 2002 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/82966
ISSN
2015 Impact Factor: 1.16
2015 SCImago Journal Rankings: 1.176
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYu, PLHen_HK
dc.contributor.authorTam, CYCen_HK
dc.date.accessioned2010-09-06T08:35:25Z-
dc.date.available2010-09-06T08:35:25Z-
dc.date.issued2002en_HK
dc.identifier.citationEnvironmetrics, 2002, v. 13 n. 4, p. 379-396en_HK
dc.identifier.issn1180-4009en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82966-
dc.description.abstractThe ranked set sampling (RSS) technique has been shown to be superior to classical simple random sampling (SRS) in the sense that it always provides a more precise estimator of the population mean. However, it is quite often that some measurements are below the limit of detection and hence become censored. In such situations, the superiority of RSS over SRS may no longer be held. In this article we consider the problem of estimating the population mean and standard deviation based on a ranked set sample with some data being censored. Maximum likelihood estimators are proposed when the data are assumed to follow a lognormal distribution. In the case where the distribution is unknown, a variant of the Kaplan-Meier estimator is proposed in the estimation of the population mean. A simulation study is conducted to compare the performance of the proposed RSS estimators with the corresponding SRS estimators. The impact of imperfect judgment ranking is also discussed. The proposed methods are applied to a real data set on mercury concentration in swordfish. Copyright © 2002 John Wiley & Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/6285en_HK
dc.relation.ispartofEnvironmetricsen_HK
dc.rightsEnvironmetrics. Copyright © John Wiley & Sons Ltd.en_HK
dc.subjectJudgment rankingen_HK
dc.subjectKaplan-Meier methoden_HK
dc.subjectLeft censoringen_HK
dc.subjectMaximum likelihood methoden_HK
dc.subjectRanked set samplingen_HK
dc.subjectSimple random samplingen_HK
dc.titleRanked set sampling in the presence of censored dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1180-4009&volume=13&spage=379&epage=396&date=2002&atitle=Ranked+set+sampling+in+the+presence+of+censored+dataen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/env.552en_HK
dc.identifier.scopuseid_2-s2.0-0036278840en_HK
dc.identifier.hkuros66478en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036278840&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume13en_HK
dc.identifier.issue4en_HK
dc.identifier.spage379en_HK
dc.identifier.epage396en_HK
dc.identifier.isiWOS:000176230500005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK
dc.identifier.scopusauthoridTam, CYC=7201442992en_HK

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