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...
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Edizione: | 1st ed. 2007. |
| Serie: | Springer Series in Statistics
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| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
L'impression du document génère 353 p. Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Correlated Data Analysis: Modeling, Analytics, and Applications, Texte imprimé, 9780387565583 • Correlated Data Analysis: Modeling, Analytics, and Applications, Texte imprimé, 9781441924407 • Correlated data analysis, modeling, analytics, and applications, Peter X.-K. Song, New York, Springer, 2007, 1 vol. (XV-346 p.), Springer series in statistics, 978-0-387-71392-2 |
Sommario:
- 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