Basic and Advanced Bayesian Structural Equation Modeling

With Applications in the Medical and Behavioral Sciences

Nonfiction, Health & Well Being, Medical, Ailments & Diseases, Infectious Diseases, Epidemiology, Psychology, Physiological Psychology, Science & Nature, Mathematics, Statistics
Cover of the book Basic and Advanced Bayesian Structural Equation Modeling by Sik-Yum Lee, Xin-Yuan Song, Wiley
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Sik-Yum Lee, Xin-Yuan Song ISBN: 9781118358870
Publisher: Wiley Publication: July 5, 2012
Imprint: Wiley Language: English
Author: Sik-Yum Lee, Xin-Yuan Song
ISBN: 9781118358870
Publisher: Wiley
Publication: July 5, 2012
Imprint: Wiley
Language: English

This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables.

Basic and Advanced Bayesian Structural Equation Model**ing introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored.

Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing.

  • Introduces the Bayesian approach to SEMs, including discussion on the selection of prior distributions, and data augmentation.
  • Demonstrates how to utilize the recent powerful tools in statistical computing including, but not limited to, the Gibbs sampler, the Metropolis-Hasting algorithm, and path sampling for producing various statistical results such as Bayesian estimates and Bayesian model comparison statistics in the analysis of basic and advanced SEMs.
  • Discusses the Bayes factor, Deviance Information Criterion (DIC), and $L_\nu$-measure for Bayesian model comparison.
  • Introduces a number of important generalizations of SEMs, including multilevel and mixture SEMs, latent curve models and longitudinal SEMs, semiparametric SEMs and those with various types of discrete data, and nonparametric structural equations.
  • Illustrates how to use the freely available software WinBUGS to produce the results.
  • Provides numerous real examples for illustrating the theoretical concepts and computational procedures that are presented throughout the book.

Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables.

Basic and Advanced Bayesian Structural Equation Model**ing introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored.

Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing.

Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.

More books from Wiley

Cover of the book Case Studies in Modern Drug Discovery and Development by Sik-Yum Lee, Xin-Yuan Song
Cover of the book The Israel-Palestine Conflict by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Handbook of Alcoholic Beverages by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Business Continuity Management by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Using Technology to Enhance Clinical Supervision by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Plate Boundaries and Natural Hazards by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Clinical Psychology by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Authentic Marketing by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Free Space Optical Networks for Ultra-Broad Band Services by Sik-Yum Lee, Xin-Yuan Song
Cover of the book CompTIA Linux+ and LPIC Practice Tests by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Diabetes Meal Planning and Nutrition For Dummies by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Uncertainty Theories and Multisensor Data Fusion by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Mathematical and Computational Methods and Algorithms in Biomechanics by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Shelter Medicine for Veterinarians and Staff by Sik-Yum Lee, Xin-Yuan Song
Cover of the book Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Learning and Memory by Sik-Yum Lee, Xin-Yuan Song
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy