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Article: Calibrated interpolated confidence intervals for population quantiles

TitleCalibrated interpolated confidence intervals for population quantiles
Authors
KeywordsBandwidth
Calibration
Interpolation
Quantile
Smoothed bootstrap
Issue Date2005
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2005, v. 92 n. 1, p. 234-241 How to Cite?
AbstractBeran & Hall's (1993) simple linear interpolation provides a very convenient approach for constructing nonparametric confidence intervals for population quantiles based on a random sample of size n. We show that the coverage error of the interpolated interval, which is of order O(n -1), can be improved upon by calibrating the nominal coverage level. Three distinct methods of calibration are considered. The analytical and Monte Carlo methods succeed in reducing the order of coverage error to O(n -3/2), while the smoothed bootstrap method reduces it further to O(n-25/14). We provide guidelines for practical implementation of the calibration methods. Their performance is compared with the simple linear interpolated interval in a simulation study which confirms superiority of the calibrated intervals. © 2005 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/82864
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHo, YHSen_HK
dc.contributor.authorLee, SMSen_HK
dc.date.accessioned2010-09-06T08:34:17Z-
dc.date.available2010-09-06T08:34:17Z-
dc.date.issued2005en_HK
dc.identifier.citationBiometrika, 2005, v. 92 n. 1, p. 234-241en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82864-
dc.description.abstractBeran & Hall's (1993) simple linear interpolation provides a very convenient approach for constructing nonparametric confidence intervals for population quantiles based on a random sample of size n. We show that the coverage error of the interpolated interval, which is of order O(n -1), can be improved upon by calibrating the nominal coverage level. Three distinct methods of calibration are considered. The analytical and Monte Carlo methods succeed in reducing the order of coverage error to O(n -3/2), while the smoothed bootstrap method reduces it further to O(n-25/14). We provide guidelines for practical implementation of the calibration methods. Their performance is compared with the simple linear interpolated interval in a simulation study which confirms superiority of the calibrated intervals. © 2005 Biometrika Trust.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectBandwidthen_HK
dc.subjectCalibrationen_HK
dc.subjectInterpolationen_HK
dc.subjectQuantileen_HK
dc.subjectSmoothed bootstrapen_HK
dc.titleCalibrated interpolated confidence intervals for population quantilesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=92&spage=234&epage=241&date=2005&atitle=Calibrated+interpolated+confidence+intervals+for+population+quantilesen_HK
dc.identifier.emailLee, SMS: smslee@hku.hken_HK
dc.identifier.authorityLee, SMS=rp00726en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/92.1.234en_HK
dc.identifier.scopuseid_2-s2.0-15844375663en_HK
dc.identifier.hkuros100411en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-15844375663&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume92en_HK
dc.identifier.issue1en_HK
dc.identifier.spage234en_HK
dc.identifier.epage241en_HK
dc.identifier.isiWOS:000228099300018-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridHo, YHS=8378552000en_HK
dc.identifier.scopusauthoridLee, SMS=24280225500en_HK
dc.identifier.citeulike166659-

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