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Structural Equation Modeling. A Bayesian Approach. Wiley Series in Probability and Statistics - Product Image

Structural Equation Modeling. A Bayesian Approach. Wiley Series in Probability and Statistics

  • Published: January 2007
  • 458 Pages
  • John Wiley and Sons Ltd

***Winner of the 2008 Ziegel Prize for outstanding new book of the year***

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 grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples.

Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances.

- Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results.
- Discusses the Bayes factor and Deviance Information Criterion (DIC) READ MORE >

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.

"This book is a welcome addition to any library and should be a valuable resource for research and teaching." (Technometrics, August 2008)

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Hard Copy (Hard Back) The book will be shipped to you. The cover has a hard back.
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