Linear Mixed Models for Longitudinal Data
This paperback edition is a reprint of the 2000 edition. 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 m...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2000, 2000
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| Edition: | 1st ed. 2000 |
| Series: | Springer Series in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Examples
- A Model for Longitudinal Data
- Exploratory Data Analysis
- Estimation of the Marginal Model
- Inference for the Marginal Model
- Inference for the Random Effects
- Fitting Linear Mixed Models with SAS
- General Guidelines for Model Building
- Exploring Serial Correlation
- Local Influence for the Linear Mixed Model
- The Heterogeneity Model
- Conditional Linear Mixed Models
- Exploring Incomplete Data
- Joint Modeling of Measurements and Missingness
- Simple Missing Data Methods
- Selection Models
- Pattern-Mixture Models
- Sensitivity Analysis for Selection Models
- Sensitivity Analysis for Pattern-Mixture Models
- How Ignorable Is Missing At Random?
- The Expectation-Maximization Algorithm
- Design Considerations
- Case Studies