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- Publisher Website: 10.1080/10485259908832796
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Article: Nonparametric estimation of quantile density function for truncated and censored data
Title | Nonparametric estimation of quantile density function for truncated and censored data |
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Authors | |
Keywords | Kernel Estimator Nearest Neighbor Estimator Optimal Bandwidth Quantile Density Function Random Bandwidth Truncating And Censoring |
Issue Date | 1999 |
Publisher | Taylor & 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/172091 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.440 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, Y | en_US |
dc.contributor.author | Yip, PSF | en_US |
dc.date.accessioned | 2012-10-30T06:20:05Z | - |
dc.date.available | 2012-10-30T06:20:05Z | - |
dc.date.issued | 1999 | en_US |
dc.identifier.citation | Journal Of Nonparametric Statistics, 1999, v. 12 n. 1, p. 17-39 | en_US |
dc.identifier.issn | 1048-5252 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/172091 | - |
dc.description.abstract | In 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.language | eng | en_US |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp | en_US |
dc.relation.ispartof | Journal of Nonparametric Statistics | en_US |
dc.subject | Kernel Estimator | en_US |
dc.subject | Nearest Neighbor Estimator | en_US |
dc.subject | Optimal Bandwidth | en_US |
dc.subject | Quantile Density Function | en_US |
dc.subject | Random Bandwidth | en_US |
dc.subject | Truncating And Censoring | en_US |
dc.title | Nonparametric estimation of quantile density function for truncated and censored data | en_US |
dc.type | Article | en_US |
dc.identifier.email | Yip, PSF: sfpyip@hku.hk | en_US |
dc.identifier.authority | Yip, PSF=rp00596 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1080/10485259908832796 | - |
dc.identifier.scopus | eid_2-s2.0-0347140930 | en_US |
dc.identifier.hkuros | 60189 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-0347140930&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.spage | 17 | en_US |
dc.identifier.epage | 39 | en_US |
dc.identifier.isi | WOS:000085497400002 | - |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Zhou, Y=24292254900 | en_US |
dc.identifier.scopusauthorid | Yip, PSF=7102503720 | en_US |
dc.identifier.issnl | 1026-7654 | - |