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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Dunson, David (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Lecture Notes in Statistics, 192
Temas:
Probabilities.
Statistics .
Probability Theory.
Statistical Theory and Methods.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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