Marginal Models: For Dependent, Clustered, and Longitudinal Categorical Data

Description

Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show.

In the social, behavioral, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. The marginal models of this book provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level.


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1. Ebooks list page : 2621
2. Antedependence Models for Longitudinal Data (Monographs on Statistics and Applied Probability)
3. Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics)
4. Topics in Modelling of Clustered Data
5. [share_ebook] Topics in Modelling of Clustered Data
6. Analysis of Panels and Limited Dependent Variable Models
7. Actuarial Theory for Dependent Risks: Measures, Orders and Models
8. Actuarial Theory for Dependent Risks: Measures, Orders and Models
9. Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models
10. Longitudinal Data Analysis
11. Analysis of Longitudinal Data
12. Modeling Longitudinal Data
13. Longitudinal Data and SAS: A Programmer's Guide
14. A Primer in Longitudinal Data Analysis
15. A Primer in Longitudinal Data Analysis
16. Visualization of Categorical Data