Random Effect and Latent Variable Model Selection

• Corporate Author: SpringerLink (Online service)
• Other Authors: Dunson, David B. (Editor)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2008.
• Series: Lecture Notes in Statistics, 192
• Subjects: Mathematics.; Probabilities.; Statistics.; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.

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...

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.