File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Nonparametric estimation of quantile density function for truncated and censored data

TitleNonparametric estimation of quantile density function for truncated and censored data
Authors
Zhou, YYip, PSF
KeywordsKernel Estimator
Nearest Neighbor Estimator
Optimal Bandwidth
Quantile Density Function
Random Bandwidth
Truncating And Censoring
Issue Date1999
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp
Citation
Journal Of Nonparametric Statistics, 1999, v. 12 n. 1, p. 17-39 How to Cite?
DOI: http://dx.doi.org/10.1080/10485259908832796
AbstractIn this paper we investigate the asymptotic properties of two types of kernel estimators for the quantile density function when the data are both randomly censored and truncated. We derive some laws of the logarithm for the maximal deviation between fixed bandwidth kernel estimators or random bandwidth kernel estimators and the true underlying quantile density function. Extensions to higher derivatives are included. The results are used to obtain the optimal bandwidth with respect to almost sure uniform convergence.
Persistent Identifierhttp://hdl.handle.net/10722/172091
ISSN
1048-5252
2021 Impact Factor: 1.012
2020 SCImago Journal Rankings: 0.735
ISI Accession Number ID
WOS:000085497400002
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yen_US
dc.contributor.authorYip, PSFen_US
dc.date.accessioned2012-10-30T06:20:05Z-
dc.date.available2012-10-30T06:20:05Z-
dc.date.issued1999en_US
dc.identifier.citationJournal Of Nonparametric Statistics, 1999, v. 12 n. 1, p. 17-39en_US
dc.identifier.issn1048-5252en_US
dc.identifier.urihttp://hdl.handle.net/10722/172091-
dc.description.abstractIn this paper we investigate the asymptotic properties of two types of kernel estimators for the quantile density function when the data are both randomly censored and truncated. We derive some laws of the logarithm for the maximal deviation between fixed bandwidth kernel estimators or random bandwidth kernel estimators and the true underlying quantile density function. Extensions to higher derivatives are included. The results are used to obtain the optimal bandwidth with respect to almost sure uniform convergence.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.aspen_US
dc.relation.ispartofJournal of Nonparametric Statisticsen_US
dc.subjectKernel Estimatoren_US
dc.subjectNearest Neighbor Estimatoren_US
dc.subjectOptimal Bandwidthen_US
dc.subjectQuantile Density Functionen_US
dc.subjectRandom Bandwidthen_US
dc.subjectTruncating And Censoringen_US
dc.titleNonparametric estimation of quantile density function for truncated and censored dataen_US
dc.typeArticleen_US
dc.identifier.emailYip, PSF: sfpyip@hku.hken_US
dc.identifier.authorityYip, PSF=rp00596en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1080/10485259908832796-
dc.identifier.scopuseid_2-s2.0-0347140930en_US
dc.identifier.hkuros60189-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0347140930&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume12en_US
dc.identifier.issue1en_US
dc.identifier.spage17en_US
dc.identifier.epage39en_US
dc.identifier.isiWOS:000085497400002-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridZhou, Y=24292254900en_US
dc.identifier.scopusauthoridYip, PSF=7102503720en_US
dc.identifier.issnl1026-7654-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats