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...
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| Format: | eBook |
| Language: | English |
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New York, NY
Springer New York
2008, 2008
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| Edition: | 1st ed. 2008 |
| Series: | Lecture Notes in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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