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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Dunson, David B. (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2008.
Σειρά:Lecture Notes in Statistics, 192
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.