File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Robust testing with generalized partial linear models for longitudinal data

TitleRobust testing with generalized partial linear models for longitudinal data
Authors
KeywordsB-spline
Estimating equations
Generalized linear models
Longitudinal data
Robust estimation
Robust testing
Issue Date2008
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspi
Citation
Journal Of Statistical Planning And Inference, 2008, v. 138 n. 6, p. 1871-1883 How to Cite?
AbstractBy approximating the nonparametric component using a regression spline in generalized partial linear models (GPLM), robust generalized estimating equations (GEE), involving bounded score function and leverage-based weighting function, can be used to estimate the regression parameters in GPLM robustly for longitudinal data or clustered data. In this paper, score test statistics are proposed for testing the regression parameters with robustness, and their asymptotic distributions under the null hypothesis and a class of local alternative hypotheses are studied. The proposed score tests reply on the estimation of a smaller model without the testing parameters involved, and perform well in the simulation studies and real data analysis conducted in this paper. © 2007 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/59886
ISSN
2015 Impact Factor: 0.727
2015 SCImago Journal Rankings: 1.090
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhou, Jen_HK
dc.contributor.authorZhu, Zen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-05-31T03:59:25Z-
dc.date.available2010-05-31T03:59:25Z-
dc.date.issued2008en_HK
dc.identifier.citationJournal Of Statistical Planning And Inference, 2008, v. 138 n. 6, p. 1871-1883en_HK
dc.identifier.issn0378-3758en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59886-
dc.description.abstractBy approximating the nonparametric component using a regression spline in generalized partial linear models (GPLM), robust generalized estimating equations (GEE), involving bounded score function and leverage-based weighting function, can be used to estimate the regression parameters in GPLM robustly for longitudinal data or clustered data. In this paper, score test statistics are proposed for testing the regression parameters with robustness, and their asymptotic distributions under the null hypothesis and a class of local alternative hypotheses are studied. The proposed score tests reply on the estimation of a smaller model without the testing parameters involved, and perform well in the simulation studies and real data analysis conducted in this paper. © 2007 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspien_HK
dc.relation.ispartofJournal of Statistical Planning and Inferenceen_HK
dc.rightsJournal of Statistical Planning and Inference. Copyright © Elsevier BV.en_HK
dc.subjectB-splineen_HK
dc.subjectEstimating equationsen_HK
dc.subjectGeneralized linear modelsen_HK
dc.subjectLongitudinal dataen_HK
dc.subjectRobust estimationen_HK
dc.subjectRobust testingen_HK
dc.titleRobust testing with generalized partial linear models for longitudinal dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0378-3758&volume=138&spage=1871&epage=1883&date=2008&atitle=Robust+testing+with+generalized+partial+linear+models+for+longitudinal+dataen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jspi.2007.07.007en_HK
dc.identifier.scopuseid_2-s2.0-39449084980en_HK
dc.identifier.hkuros149899en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-39449084980&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume138en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1871en_HK
dc.identifier.epage1883en_HK
dc.identifier.isiWOS:000254814200026-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridZhou, J=9043425500en_HK
dc.identifier.scopusauthoridZhu, Z=23487505000en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.citeulike4640603-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats