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Correlated Data Analysis: Modeling, Analytics, and Applications
This book presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generali...
| Cote: | Libro Electrónico |
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| Auteur principal: | |
| Collectivité auteur: | |
| Format: | Électronique eBook |
| Langue: | Inglés |
| Publié: |
New York, NY :
Springer New York : Imprint: Springer,
2007.
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| Édition: | 1st ed. 2007. |
| Collection: | Springer Series in Statistics,
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| Sujets: | |
| Accès en ligne: | Texto Completo |
Table des matières:
- and Examples
- Dispersion Models
- Inference Functions
- Modeling Correlated Data
- Marginal Generalized Linear Models
- Vector Generalized Linear Models
- Mixed-Effects Models: Likelihood-Based Inference
- Mixed-Effects Models: Bayesian Inference
- Linear Predictors
- Generalized State Space Models
- Generalized State Space Models for Longitudinal Binomial Data
- Generalized State Space Models for Longitudinal Count Data
- Missing Data in Longitudinal Studies.