A cautionary note on generalized linear models for covariance of unbalanced longitudinal data. (English) Zbl 1229.62096
Summary: Missing data in longitudinal studies can create enormous challenges in data analyses 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 [M. Pourahmadi, Biometrika 87, No. 2, 425–435 (2000; Zbl 0954.62091)]. 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 this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations.
MSC:
| 62J12 | Generalized linear models (logistic models) |
| 62H12 | Estimation in multivariate analysis |
| 65C60 | Computational problems in statistics (MSC2010) |
Citations:
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