Random Effect and Latent Variable Model Selection
Random effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predicti...
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2008. |
| Collection: | Lecture Notes in Statistics
192 |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Random effect and latent variable model selection, David B. Dunson, editor, New York, Springer, 2008, 1 vol. (ix-169 p.), Lecture notes in statistics, 978-0-387-76720-8 |
Table des matières:
- Random Effects Models Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models Variance Component Testing in Generalized Linear Mixed Models for Longitudinal/Clustered Data and other Related Topics Bayesian Model Uncertainty in Mixed Effects Models Bayesian Variable Selection in Generalized Linear Mixed Models Factor Analysis and Structural Equations Models A Unified Approach to Two-Level Structural Equation Models and Linear Mixed Effects Models Bayesian Model Comparison of Structural Equation Models Bayesian Model Selection in Factor Analytic Models