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Article: On the asymptotics of marginal regression splines with longitudinal data

TitleOn the asymptotics of marginal regression splines with longitudinal data
Authors
KeywordsAsymptotic bias
B-spline
Generalized estimating equation
Generalized linear model
Least squares
Longitudinal data
Issue Date2008
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2008, v. 95 n. 4, p. 907-917 How to Cite?
AbstractThere have been studies on how the asymptotic efficiency of a nonparametric function estimator depends on the handling of the within-cluster correlation when nonparametric regression models are used on longitudinal or cluster data. In particular, methods based on smoothing splines and local polynomial kernels exhibit different behaviour. We show that the generalized estimation equations based on weighted least squares regression splines for the nonparametric function have an interesting property: the asymptotic bias of the estimator does not depend on the working correlation matrix, but the asymptotic variance, and therefore the mean squared error, is minimized when the true correlation structure is specified. This property of the asymptotic bias distinguishes regression splines from smoothing splines. © 2008 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/59857
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grant Council
National Natural Science Foundation of China
U.S. National Science Foundation
Funding Information:

The research was partially supported by grants from the Hong Kong Research Grant Council, the National Natural Science Foundation of China and the U.S. National Science Foundation. The authors thank an associate editor and a referee for their helpful comments and suggestions on an earlier draft of the paper.

References

 

DC FieldValueLanguage
dc.contributor.authorZhu, Zen_HK
dc.contributor.authorFung, WKen_HK
dc.contributor.authorHe, Xen_HK
dc.date.accessioned2010-05-31T03:58:53Z-
dc.date.available2010-05-31T03:58:53Z-
dc.date.issued2008en_HK
dc.identifier.citationBiometrika, 2008, v. 95 n. 4, p. 907-917en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59857-
dc.description.abstractThere have been studies on how the asymptotic efficiency of a nonparametric function estimator depends on the handling of the within-cluster correlation when nonparametric regression models are used on longitudinal or cluster data. In particular, methods based on smoothing splines and local polynomial kernels exhibit different behaviour. We show that the generalized estimation equations based on weighted least squares regression splines for the nonparametric function have an interesting property: the asymptotic bias of the estimator does not depend on the working correlation matrix, but the asymptotic variance, and therefore the mean squared error, is minimized when the true correlation structure is specified. This property of the asymptotic bias distinguishes regression splines from smoothing splines. © 2008 Biometrika Trust.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectAsymptotic biasen_HK
dc.subjectB-splineen_HK
dc.subjectGeneralized estimating equationen_HK
dc.subjectGeneralized linear modelen_HK
dc.subjectLeast squaresen_HK
dc.subjectLongitudinal dataen_HK
dc.titleOn the asymptotics of marginal regression splines with longitudinal dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=95&issue=4&spage=907&epage=917&date=2008&atitle=On+the+asymptotics+of+marginal+regression+splines+with+longitudinal+dataen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/asn041en_HK
dc.identifier.scopuseid_2-s2.0-57249114756en_HK
dc.identifier.hkuros163121en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-57249114756&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume95en_HK
dc.identifier.issue4en_HK
dc.identifier.spage907en_HK
dc.identifier.epage917en_HK
dc.identifier.eissn1464-3510-
dc.identifier.isiWOS:000261279900009-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZhu, Z=23487505000en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.scopusauthoridHe, X=7404407842en_HK

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