Lee, Sik-Yum
Structural Equation Modelling: A Bayesian Approach
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) for model comparison.
- Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations.
- Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology.
- Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets.
Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.
Nyckelord: MATHEMATICS / Probability & Statistics / General MAT029000
- Författare
- Lee, Sik-Yum
- Utgivare
- John Wiley and Sons, Inc.
- Utgivningsår
- 2007
- Språk
- en
- Utgåva
- 1
- Serie
- Wiley Series in Probability and Statistics
- Sidantal
- 458 sidor
- Kategori
- Naturvetenskaper
- Format
- E-bok
- eISBN (PDF)
- 9780470024249
- Tryckt ISBN
- 9780470024232