Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1541-0420.2007.00842.x
DC FieldValue
dc.titleWeighted rank regression for clustered data analysis
dc.contributor.authorWang, Y.-G.
dc.contributor.authorZhao, Y.
dc.date.accessioned2016-11-16T11:06:03Z
dc.date.available2016-11-16T11:06:03Z
dc.date.issued2008-03
dc.identifier.citationWang, Y.-G., Zhao, Y. (2008-03). Weighted rank regression for clustered data analysis. Biometrics 64 (1) : 39-45. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1541-0420.2007.00842.x
dc.identifier.issn0006341X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/130446
dc.description.abstractWe consider ranked-based regression models for clustered data analysis. A weighted Wilcoxon rank method is proposed to take account of within-cluster correlations and varying cluster sizes. The asymptotic normality of the resulting estimators is established. A method to estimate covariance of the estimators is also given, which can bypass estimation of the density function. Simulation studies are carried out to compare different estimators for a number of scenarios on the correlation structure, presence/absence of outliers and different correlation values. The proposed methods appear to perform well, in particular, the one incorporating the correlation in the weighting achieves the highest efficiency and robustness against misspecification of correlation structure and outliers. A real example is provided for illustration. © 2007, The International Biometric Society.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1111/j.1541-0420.2007.00842.x
dc.sourceScopus
dc.subjectClustered data
dc.subjectCovariance estimation
dc.subjectDependent data
dc.subjectEstimating functions
dc.subjectLongitudinal data
dc.subjectRank estimation
dc.subjectRepeated measures
dc.subjectWilcoxon score
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1111/j.1541-0420.2007.00842.x
dc.description.sourcetitleBiometrics
dc.description.volume64
dc.description.issue1
dc.description.page39-45
dc.description.codenBIOMA
dc.identifier.isiut000253632200005
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