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Article: Weighted empirical likelihood for generalized linear models with longitudinal data
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TitleWeighted empirical likelihood for generalized linear models with longitudinal data
 
AuthorsBai, Y2
Fung, WK1
Zhu, Z3
 
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
 
CitationJournal Of Statistical Planning And Inference, 2010, v. 140 n. 11, p. 3446-3456 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.jspi.2010.05.007
 
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.
 
ISSN0378-3758
2013 Impact Factor: 0.598
2013 SCImago Journal Rankings: 1.132
 
DOIhttp://dx.doi.org/10.1016/j.jspi.2010.05.007
 
ISI Accession Number IDWOS:000279997000044
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.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorBai, Y
 
dc.contributor.authorFung, WK
 
dc.contributor.authorZhu, Z
 
dc.date.accessioned2011-08-26T14:27:39Z
 
dc.date.available2011-08-26T14:27:39Z
 
dc.date.issued2010
 
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.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationJournal Of Statistical Planning And Inference, 2010, v. 140 n. 11, p. 3446-3456 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.jspi.2010.05.007
 
dc.identifier.citeulike7263928
 
dc.identifier.doihttp://dx.doi.org/10.1016/j.jspi.2010.05.007
 
dc.identifier.epage3456
 
dc.identifier.hkuros189416
 
dc.identifier.isiWOS:000279997000044
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.

 
dc.identifier.issn0378-3758
2013 Impact Factor: 0.598
2013 SCImago Journal Rankings: 1.132
 
dc.identifier.issue11
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-77954033636
 
dc.identifier.spage3446
 
dc.identifier.urihttp://hdl.handle.net/10722/137542
 
dc.identifier.volume140
 
dc.languageeng
 
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspi
 
dc.publisher.placeNetherlands
 
dc.relation.ispartofJournal of Statistical Planning and Inference
 
dc.relation.referencesReferences in Scopus
 
dc.subjectConfidence region
 
dc.subjectEmpirical likelihood
 
dc.subjectGeneralized linear models
 
dc.subjectLongitudinal data
 
dc.titleWeighted empirical likelihood for generalized linear models with longitudinal data
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Shanghai University of Finance and EcoNomics
  3. Fudan University