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Article: Penalized quadratic inference functions for single-index models with longitudinal data

TitlePenalized quadratic inference functions for single-index models with longitudinal data
Authors
Bai, YFung, WKZhu, ZY
Keywords46N30
Longitudinal data
P-splines
Quadratic inference functions
Single-index models
Issue Date2009
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal Of Multivariate Analysis, 2009, v. 100 n. 1, p. 152-161 How to Cite?
DOI: http://dx.doi.org/10.1016/j.jmva.2008.04.004
AbstractIn this paper, we focus on single-index models for longitudinal data. We propose a procedure to estimate the single-index component and the unknown link function based on the combination of the penalized splines and quadratic inference functions. It is shown that the proposed estimation method has good asymptotic properties. We also evaluate the finite sample performance of the proposed method via Monte Carlo simulation studies. Furthermore, the proposed method is illustrated in the analysis of a real data set. © 2008 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/59881
ISSN
0047-259X
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 0.837
ISI Accession Number ID
WOS:000261040700012
Funding AgencyGrant Number
Natural Science Foundation of ChinaNSFC10671038
Funding Information:

The authors thank two referees and an associate editor for their valuable comments that largely improved the presentation of the paper. This work was partly supported by the Natural Science Foundation of China under Contract no. NSFC10671038.

References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorBai, Yen_HK
dc.contributor.authorFung, WKen_HK
dc.contributor.authorZhu, ZYen_HK
dc.date.accessioned2010-05-31T03:59:19Z-
dc.date.available2010-05-31T03:59:19Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of Multivariate Analysis, 2009, v. 100 n. 1, p. 152-161en_HK
dc.identifier.issn0047-259Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/59881-
dc.description.abstractIn this paper, we focus on single-index models for longitudinal data. We propose a procedure to estimate the single-index component and the unknown link function based on the combination of the penalized splines and quadratic inference functions. It is shown that the proposed estimation method has good asymptotic properties. We also evaluate the finite sample performance of the proposed method via Monte Carlo simulation studies. Furthermore, the proposed method is illustrated in the analysis of a real data set. © 2008 Elsevier Inc. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmvaen_HK
dc.relation.ispartofJournal of Multivariate Analysisen_HK
dc.subject46N30en_HK
dc.subjectLongitudinal dataen_HK
dc.subjectP-splinesen_HK
dc.subjectQuadratic inference functionsen_HK
dc.subjectSingle-index modelsen_HK
dc.titlePenalized quadratic inference functions for single-index models with longitudinal dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0047-259X&volume=100&spage=152&epage=161&date=2009&atitle=Penalized+quadratic+inference+functions+for+single-index+models+with+longitudinal+data+en_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.jmva.2008.04.004en_HK
dc.identifier.scopuseid_2-s2.0-55049140582en_HK
dc.identifier.hkuros163120en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-55049140582&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume100en_HK
dc.identifier.issue1en_HK
dc.identifier.spage152en_HK
dc.identifier.epage161en_HK
dc.identifier.isiWOS:000261040700012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridBai, Y=36084084600en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.scopusauthoridZhu, ZY=23487505000en_HK
dc.identifier.issnl0047-259X-

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