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Article: Variable selection in robust joint mean and covariance model for longitudinal data analysis

TitleVariable selection in robust joint mean and covariance model for longitudinal data analysis
Authors
Issue Date2014
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2014, v. 24 n. 2, p. 515-531 How to Cite?
AbstractIn longitudinal data analysis, a correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this article, we consider robust variable selection method in a joint mean and covariance model. We propose a set of penalized robust generalized estimating equations to simultaneously estimate the mean regression coefficients, the generalized autoregressive coefficients, and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, we develop the oracle property of the proposed robust variable selection method. Finally, a simulation study and a detailed data analysis are carried out to assess and illustrate the small sample performance; they show that the proposed method performs favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model.
Persistent Identifierhttp://hdl.handle.net/10722/199235
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, XY-
dc.contributor.authorFung, WK-
dc.contributor.authorZhu, ZY-
dc.date.accessioned2014-07-22T01:09:54Z-
dc.date.available2014-07-22T01:09:54Z-
dc.date.issued2014-
dc.identifier.citationStatistica Sinica, 2014, v. 24 n. 2, p. 515-531-
dc.identifier.issn1017-0405-
dc.identifier.urihttp://hdl.handle.net/10722/199235-
dc.description.abstractIn longitudinal data analysis, a correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this article, we consider robust variable selection method in a joint mean and covariance model. We propose a set of penalized robust generalized estimating equations to simultaneously estimate the mean regression coefficients, the generalized autoregressive coefficients, and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, we develop the oracle property of the proposed robust variable selection method. Finally, a simulation study and a detailed data analysis are carried out to assess and illustrate the small sample performance; they show that the proposed method performs favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model.-
dc.languageeng-
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/-
dc.relation.ispartofStatistica Sinica-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleVariable selection in robust joint mean and covariance model for longitudinal data analysis-
dc.typeArticle-
dc.identifier.emailFung, TWK: wingfung@hku.hk-
dc.identifier.authorityFung, TWK=rp00696-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5705/ss.2011.251-
dc.identifier.hkuros231264-
dc.identifier.volume24-
dc.identifier.issue2-
dc.identifier.spage515-
dc.identifier.epage531-
dc.identifier.isiWOS:000337114800001-
dc.publisher.placeTaiwan, Republic of China-

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