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Department seminar by Professor Chenlei Leng, National University of SingaporeExport this event to calendar

Wednesday, December 12, 2012 — 2:00 PM EST

"Modeling covariances in longitudinal studies"

In longitudinal studies, it is crucially important to synthetically understand the dynamics in the mean function, the variance function, and covariations of the repeated or clustered measurements. For the covariance structure, parsimoniously modeling approaches such as those utilizing the Cholesky type decompositions have been demonstrated effective in longitudinal data modeling and analysis. However, direct yet parsimonious approaches for revealing the covariance and correlation structures among longitudinal data remain less explored, and existing approaches may face difficulty when interpreting the correlation structures of longitudinal data. We propose a novel unified joint mean-variance-correlation modeling approach for longitudinal data analysis. By applying hyperspherical coordinates, we propose to model the correlation matrix of dependent longitudinal measurements by unconstrained parameters. The proposed modeling framework is parsimonious, interpretable, and flexible, and it guarantees the resulting correlation matrix to be non-negative definite. Extensive data examples and simulations support the effectiveness of the proposed approach.

Location 
MC - Mathematics & Computer Building
3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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