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Presenting effective approaches to address missing data, measurement errors, censoring, and outliers in longitudinal data, this book covers linear, nonlinear, generalized linear, nonparametric, and semiparametric mixed effects models. It links each mixed effects model with the corresponding class of regression model for cross-sectional data and discusses computational strategies for likelihood estimations of mixed effects models. The author briefly describes generalized estimating equations methods and Bayesian mixed effects models and explains how to implement standard models using R and S-Plus. The real-world data examples used throughout encompass studies on mental distress, AIDS, and more.
Chapman & Hall/CRC; November 2009
440 pages; ISBN 9781420074086
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440 pages; ISBN 9781420074086
Read online, or download in secure PDF format
