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Article: Robust estimation of the generalised partial linear model with missing covariates

TitleRobust estimation of the generalised partial linear model with missing covariates
Authors
Keywordsgeneralised partial linear models
missing covariates
regression spline
robustness
weighted method
Issue Date2012
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, 2012, v. 24 n. 2, p. 517-530 How to Cite?
AbstractIn this paper, we propose robust estimation of the generalised partial linear model with covariates missing at random. The developed approach integrated the robust method and the method for dealing with missing data. Under some regularity conditions, we establish the asymptotic normality of the proposed estimator of the regression coefficients and show that the proposed estimator of the nonparametric function can achieve the optimal rate of convergence. It can be observed that the regression spline approach avoids some of the intricacies associated with the kernel method, and the robust estimation and inference can be carried out operationally as if a generalised linear model were used. Simulation studies are conducted to investigate the robustness of the proposed method. At the end, the proposed method is applied to a real data analysis for illustration. © 2012 Copyright American Statistical Association and Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/159902
ISSN
2015 Impact Factor: 0.446
2015 SCImago Journal Rankings: 0.980
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQin, Gen_HK
dc.contributor.authorZhu, Zen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2012-08-16T05:59:09Z-
dc.date.available2012-08-16T05:59:09Z-
dc.date.issued2012en_HK
dc.identifier.citationJournal Of Nonparametric Statistics, 2012, v. 24 n. 2, p. 517-530en_HK
dc.identifier.issn1048-5252en_HK
dc.identifier.urihttp://hdl.handle.net/10722/159902-
dc.description.abstractIn this paper, we propose robust estimation of the generalised partial linear model with covariates missing at random. The developed approach integrated the robust method and the method for dealing with missing data. Under some regularity conditions, we establish the asymptotic normality of the proposed estimator of the regression coefficients and show that the proposed estimator of the nonparametric function can achieve the optimal rate of convergence. It can be observed that the regression spline approach avoids some of the intricacies associated with the kernel method, and the robust estimation and inference can be carried out operationally as if a generalised linear model were used. Simulation studies are conducted to investigate the robustness of the proposed method. At the end, the proposed method is applied to a real data analysis for illustration. © 2012 Copyright American Statistical Association and Taylor & Francis.en_HK
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.aspen_HK
dc.relation.ispartofJournal of Nonparametric Statisticsen_HK
dc.subjectgeneralised partial linear modelsen_HK
dc.subjectmissing covariatesen_HK
dc.subjectregression splineen_HK
dc.subjectrobustnessen_HK
dc.subjectweighted methoden_HK
dc.titleRobust estimation of the generalised partial linear model with missing covariatesen_HK
dc.typeArticleen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/10485252.2012.662972en_HK
dc.identifier.scopuseid_2-s2.0-84860800634en_HK
dc.identifier.hkuros203693en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84860800634&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue2en_HK
dc.identifier.spage517en_HK
dc.identifier.epage530en_HK
dc.identifier.isiWOS:000303576400016-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridQin, G=19640646400en_HK
dc.identifier.scopusauthoridZhu, Z=23487505000en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK

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