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Structural Equation Modeling: A Bayesian Approach Book

Structural Equation Modeling: A Bayesian Approach
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  • Structural Equation Modeling: A Bayesian Approach
  • Written by author Sik-Yum Lee
  • Published by Wiley, John & Sons, Incorporated, March 2007
  • Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have
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Authors

About the Author.

Preface.

Chapter 1. Introduction.

Chapter 2. Some Basic Structural Equation Models.

Chapter 3. Covariance Structure Analysis.

Chapter 4. Bayesian Estimation of Structural Equation Models.

Chapter 5. Model Comparison and Model Checking.

Chapter 6. Structural Equation Models with Continuous and Ordered Categorical Variables.

Chapter 7. Structural Equation Models with Dichotomous Variables.

Chapter 8. Nonlinear Structural Equation Models.

Chapter 9. Two-level Nonlinear Structural Equation Models.

Chapter 10. Multisample Analysis of Structural Equation Models.

Chapter 11. Finite Mixtures in Structural Equation Models.

Chapter 12. Structural Equation Models with Missing Data.

Chapter 13. Structural Equation Models with Exponential Family of Distributions.

Chapter 14. Conclusion.

Index.


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