This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance
Linear Mixed Models for Longitudinal Data
โ Scribed by Geert Verbeke, Geert Molenberghs
- Publisher
- Springer
- Year
- 2000
- Tongue
- English
- Leaves
- 579
- Edition
- Corrected
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this book puts major emphasis on exploratory data analysis for all aspects of the model. Several variations to the conventional linear mixed model are discussed. Most analyses were done with the Mixed procedure of the SAS software package, however, other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion.
๐ SIMILAR VOLUMES
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance
<p><P>This paperback edition is a reprint of the 2000 edition.</P><P></P><P>This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, suc
<p>This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effec
<p><p>This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that acc