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...
Συγγραφή απο Οργανισμό/Αρχή: | |
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Άλλοι συγγραφείς: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
New York, NY :
Springer New York : Imprint: Springer,
2008.
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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.