The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package.
Models for Discrete Longitudinal Data (Springer Series in Statistics)
โ Scribed by Geert Molenberghs, Geert Verbeke
- Publisher
- Springer
- Year
- 2005
- Tongue
- English
- Leaves
- 679
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow.
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<p><span>Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis
<P>This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principle