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

File Download Links for fulltext
(May Require Subscription)
Supplementary

  • Basic View
  • Metadata View
  • XML View
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
2011 Impact Factor: 0.716
2011 SCImago Journal Rankings: 0.046
DOIhttp://dx.doi.org/10.1016/j.jspi.2010.05.007
ReferencesReferences in Scopus
DC Field
Value
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
2011 Impact Factor: 0.716
2011 SCImago Journal Rankings: 0.046
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
Author Affiliations
  1. The University of Hong Kong
  2. Shanghai University of Finance and EcoNomics
  3. Fudan University