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Activity Number:
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44
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307754 |
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Title:
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Modeling of Mean-Covariance Structures in Generalized Estimating Equations for Longitudinal Data
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Author(s):
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Jianxin Pan*+
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Companies:
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University of Manchester
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Address:
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School of Mathematics, University of Manchester, Manchester, M60 1QD, United Kingdom
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Keywords:
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Cholesky decomposition ; Efficiency ; Generalized estimating equation ; Longitudinal data ; Misspecification of covariance structure ; Modelling of mean-covariance structures
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Abstract:
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When used for modeling longitudinal data generalized estimating equations specify a working structure for the within-subject covariance matrices, aiming to produce efficient parameter estimators. However, misspecification of the working covariance structure may lead to a large loss of efficiency of the estimators of the mean parameters. In this talk I will introduce an approach for joint modeling of the mean and covariance structures for longitudinal data within the framework of generalized estimating equations. The resulting estimators for the mean and covariance parameters are shown to be consistent and asymptotically Normally distributed. Real data analysis and simulation studies show that the proposed approach produces efficient estimators for both the mean and covariance parameters.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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