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Article: Joint estimation of mean-covariance model for longitudinal data with basis function approximations

TitleJoint estimation of mean-covariance model for longitudinal data with basis function approximations
Authors
KeywordsB-splines
Basis function
BIC
Modified Cholesky decomposition
Partially linear model
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2011, v. 55 n. 2, p. 983-992 How to Cite?
AbstractWhen the selected parametric model for the covariance structure is far from the true one, the corresponding covariance estimator could have considerable bias. To balance the variability and bias of the covariance estimator, we employ a nonparametric method. In addition, as different mean structures may lead to different estimators of the covariance matrix, we choose a semiparametric model for the mean so as to provide a stable estimate of the covariance matrix. Based on the modified Cholesky decomposition of the covariance matrix, we construct the joint mean-covariance model by modeling the smooth functions using the spline method and estimate the associated parameters using the maximum likelihood approach. A simulation study and a real data analysis are conducted to illustrate the proposed approach and demonstrate the flexibility of the suggested model. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/137541
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
Funding AgencyGrant Number
Natural Science Foundation of China10931002
1091120386
Funding Information:

The authors would like to thank the Editor and the referees for their constructive comments and helpful suggestions that largely improve the presentation of the paper. The research is supported by the Natural Science Foundation of China Grant 10931002, 1091120386.

References

 

DC FieldValueLanguage
dc.contributor.authorMao, Jen_HK
dc.contributor.authorZhu, Zen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2011-08-26T14:27:39Z-
dc.date.available2011-08-26T14:27:39Z-
dc.date.issued2011en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2011, v. 55 n. 2, p. 983-992en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137541-
dc.description.abstractWhen the selected parametric model for the covariance structure is far from the true one, the corresponding covariance estimator could have considerable bias. To balance the variability and bias of the covariance estimator, we employ a nonparametric method. In addition, as different mean structures may lead to different estimators of the covariance matrix, we choose a semiparametric model for the mean so as to provide a stable estimate of the covariance matrix. Based on the modified Cholesky decomposition of the covariance matrix, we construct the joint mean-covariance model by modeling the smooth functions using the spline method and estimate the associated parameters using the maximum likelihood approach. A simulation study and a real data analysis are conducted to illustrate the proposed approach and demonstrate the flexibility of the suggested model. © 2010 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.subjectB-splinesen_HK
dc.subjectBasis functionen_HK
dc.subjectBICen_HK
dc.subjectModified Cholesky decompositionen_HK
dc.subjectPartially linear modelen_HK
dc.titleJoint estimation of mean-covariance model for longitudinal data with basis function approximationsen_HK
dc.typeArticleen_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.csda.2010.08.003en_HK
dc.identifier.scopuseid_2-s2.0-78049259985en_HK
dc.identifier.hkuros189408en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78049259985&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume55en_HK
dc.identifier.issue2en_HK
dc.identifier.spage983en_HK
dc.identifier.epage992en_HK
dc.identifier.isiWOS:000284976600004-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridMao, J=36457274700en_HK
dc.identifier.scopusauthoridZhu, Z=23487505000en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.citeulike7779179-

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