Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/27778
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dc.titleInfeasibility and efficiency of working correlation matrix in generalized estimating equations
dc.contributor.authorNG WEE TECK
dc.date.accessioned2011-10-18T18:00:42Z
dc.date.available2011-10-18T18:00:42Z
dc.date.issued2006-02-06
dc.identifier.citationNG WEE TECK (2006-02-06). Infeasibility and efficiency of working correlation matrix in generalized estimating equations. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/27778
dc.description.abstractGeneralized estimating equations (GEE) have been used extensively in the analysis of clustered and longitudinal data since the seminal paper by Liang & Zeger (1986). A key attraction of using GEE is that regression parameters remain consistent even when the 'working' correlation is misspecified. However, Crowder (1995) pointed out that there are problems with the estimation of the correlation parameters when it is misspecified and this affects the consistency of the regression parameters. This issue has been addressed by Chaganty & Shults (1999), however the estimators are asymptotically biased. The feasibility of the estimators for the correlation parameters under misspecification efficiency of the various methods of estimating the correlation parameters under misspecification are investigated in this thesis. Analytic expressions for the estimating functions using the decoupled Gaussian and Cholesky decomposition proposed by Wang & Carey (2004) are for common correlation structures such as exchangeable, AR(1) and MA(1) are also provided.
dc.language.isoen
dc.subjectEfficiency; Estimating function; Longitudinal data; CORRELATION; Repeated measures; Pseudolikelihood
dc.typeThesis
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.contributor.supervisorWANG YOUGAN
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF SCIENCE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Master's Theses (Open)

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