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Article: Likelihood-based approaches for multivariate linear models under inequality constraints for incomplete data

TitleLikelihood-based approaches for multivariate linear models under inequality constraints for incomplete data
Authors
Zheng, SGuo, JShi, NZTian, GL
KeywordsConfidence Intervals
Convergence
Ecm Algorithm
Inequality Constraints
Linear Mixed Models
Maximum Likelihood Estimation
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspi
Citation
Journal Of Statistical Planning And Inference, 2012, v. 142 n. 11, p. 2926-2942 How to Cite?
DOI: http://dx.doi.org/10.1016/j.jspi.2012.04.018
AbstractIn this paper, we consider a multivariate linear model with complete/incomplete data, where the regression coefficients are subject to a set of linear inequality restrictions. We first develop an expectation/. conditional maximization (ECM) algorithm for calculating restricted maximum likelihood estimates of parameters of interest. We then establish the corresponding convergence properties for the proposed ECM algorithm. Applications to growth curve models and linear mixed models are presented. Confidence interval construction via the double-bootstrap method is provided. Some simulation studies are performed and a real example is used to illustrate the proposed methods. © 2012 Elsevier B.V..
Persistent Identifierhttp://hdl.handle.net/10722/172500
ISSN
0378-3758
2021 Impact Factor: 1.095
2020 SCImago Journal Rankings: 0.622
ISI Accession Number ID
WOS:000306863800004
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorZheng, Sen_US
dc.contributor.authorGuo, Jen_US
dc.contributor.authorShi, NZen_US
dc.contributor.authorTian, GLen_US
dc.date.accessioned2012-10-30T06:22:49Z-
dc.date.available2012-10-30T06:22:49Z-
dc.date.issued2012en_US
dc.identifier.citationJournal Of Statistical Planning And Inference, 2012, v. 142 n. 11, p. 2926-2942en_US
dc.identifier.issn0378-3758en_US
dc.identifier.urihttp://hdl.handle.net/10722/172500-
dc.description.abstractIn this paper, we consider a multivariate linear model with complete/incomplete data, where the regression coefficients are subject to a set of linear inequality restrictions. We first develop an expectation/. conditional maximization (ECM) algorithm for calculating restricted maximum likelihood estimates of parameters of interest. We then establish the corresponding convergence properties for the proposed ECM algorithm. Applications to growth curve models and linear mixed models are presented. Confidence interval construction via the double-bootstrap method is provided. Some simulation studies are performed and a real example is used to illustrate the proposed methods. © 2012 Elsevier B.V..en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jspien_US
dc.relation.ispartofJournal of Statistical Planning and Inferenceen_US
dc.subjectConfidence Intervalsen_US
dc.subjectConvergenceen_US
dc.subjectEcm Algorithmen_US
dc.subjectInequality Constraintsen_US
dc.subjectLinear Mixed Modelsen_US
dc.subjectMaximum Likelihood Estimationen_US
dc.titleLikelihood-based approaches for multivariate linear models under inequality constraints for incomplete dataen_US
dc.typeArticleen_US
dc.identifier.emailTian, GL: gltian@hku.hken_US
dc.identifier.authorityTian, GL=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.jspi.2012.04.018en_US
dc.identifier.scopuseid_2-s2.0-84862993966en_US
dc.identifier.hkuros225945-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84862993966&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume142en_US
dc.identifier.issue11en_US
dc.identifier.spage2926en_US
dc.identifier.epage2942en_US
dc.identifier.isiWOS:000306863800004-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridZheng, S=7403146780en_US
dc.identifier.scopusauthoridGuo, J=35307009400en_US
dc.identifier.scopusauthoridShi, NZ=7004451232en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US
dc.identifier.citeulike10847447-
dc.identifier.issnl0378-3758-

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