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Article: Weighted empirical likelihood for generalized linear models with longitudinal data

TitleWeighted empirical likelihood for generalized linear models with longitudinal data
Authors
KeywordsConfidence region
Empirical likelihood
Generalized linear models
Longitudinal data
Issue Date2010
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspi
Citation
Journal Of Statistical Planning And Inference, 2010, v. 140 n. 11, p. 3446-3456 How to Cite?
Abstract
In this paper, we introduce the empirical likelihood (EL) method to longitudinal studies. By considering the dependence within subjects in the auxiliary random vectors, we propose a new weighted empirical likelihood (WEL) inference for generalized linear models with longitudinal data. We show that the weighted empirical likelihood ratio always follows an asymptotically standard chi-squared distribution no matter which working weight matrix that we have chosen, but a well chosen working weight matrix can improve the efficiency of statistical inference. Simulations are conducted to demonstrate the accuracy and efficiency of our proposed WEL method, and a real data set is used to illustrate the proposed method. © 2010 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/137542
ISSN
2013 Impact Factor: 0.598
2013 SCImago Journal Rankings: 1.132
ISI Accession Number ID
Funding AgencyGrant Number
Natural Science Foundation of China10671038
Shanghai University of Finance and Economics211
Shanghai Leading Academic Discipline ProjectB803
B210
Funding Information:

The authors thank the Editor and referees for helpful comments that largely improve the presentation of the paper. This work was partly supported by Natural Science Foundation of China (10671038), Shanghai University of Finance and Economics through Project 211 Phase III and Shanghai Leading Academic Discipline Project, Project number: B803 and B210.

References

 

Author Affiliations
  1. The University of Hong Kong
  2. Shanghai University of Finance and EcoNomics
  3. Fudan University
DC FieldValueLanguage
dc.contributor.authorBai, Yen_HK
dc.contributor.authorFung, WKen_HK
dc.contributor.authorZhu, Zen_HK
dc.date.accessioned2011-08-26T14:27:39Z-
dc.date.available2011-08-26T14:27:39Z-
dc.date.issued2010en_HK
dc.identifier.citationJournal Of Statistical Planning And Inference, 2010, v. 140 n. 11, p. 3446-3456en_HK
dc.identifier.issn0378-3758en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137542-
dc.description.abstractIn this paper, we introduce the empirical likelihood (EL) method to longitudinal studies. By considering the dependence within subjects in the auxiliary random vectors, we propose a new weighted empirical likelihood (WEL) inference for generalized linear models with longitudinal data. We show that the weighted empirical likelihood ratio always follows an asymptotically standard chi-squared distribution no matter which working weight matrix that we have chosen, but a well chosen working weight matrix can improve the efficiency of statistical inference. Simulations are conducted to demonstrate the accuracy and efficiency of our proposed WEL method, and a real data set is used to illustrate the proposed method. © 2010 Elsevier B.V.en_HK
dc.languageengen_US
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.subjectConfidence regionen_HK
dc.subjectEmpirical likelihooden_HK
dc.subjectGeneralized linear modelsen_HK
dc.subjectLongitudinal dataen_HK
dc.titleWeighted empirical likelihood for generalized linear models with longitudinal dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0378-3758&volume=140&spage=3446&epage=3456&date=2010&atitle=Weighted+empirical+likelihood+for+generalized+linear+models+with+longitudinal+dataen_US
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.2010.05.007en_HK
dc.identifier.scopuseid_2-s2.0-77954033636en_HK
dc.identifier.hkuros189416en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77954033636&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume140en_HK
dc.identifier.issue11en_HK
dc.identifier.spage3446en_HK
dc.identifier.epage3456en_HK
dc.identifier.isiWOS:000279997000044-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridBai, Y=36084084600en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.scopusauthoridZhu, Z=23487505000en_HK
dc.identifier.citeulike7263928-

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