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A Cautionary Note On Generalized Linear Models For Covariance Of Unbalanced Longitudinal Data, Jianhua Z. Huang, Min Chen, Mehdi Maadooliat, Mohsen Pourahmadi

Mathematics, Statistics and Computer Science Faculty Research and Publications

Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome …