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eBook Structural Equation Modeling: A Bayesian Approach epub

by Sik-Yum Lee

eBook Structural Equation Modeling: A Bayesian Approach epub
  • ISBN: 0470024232
  • Author: Sik-Yum Lee
  • Genre: Science
  • Subcategory: Mathematics
  • Language: English
  • Publisher: Wiley; 1 edition (March 12, 2007)
  • Pages: 458 pages
  • ePUB size: 1161 kb
  • FB2 size 1454 kb
  • Formats azw lrf rtf lit


Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of. .

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.

Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior . Sik-Yum Lee is a professor of statistics at the Chinese University of Hong Kong. in biostatistics at the University of California, Los Angeles, USA.

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.

Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and . Yes, it's a math-heavy book, but Sik-Yum Lee does a great job explaining this very different approach. Lee demonstrates Bayesian methods applied to basic models, interaction models, mixture models, multi-level models, and models with non-normal distributions.

Электронная книга "Structural Equation Modeling: A Bayesian Approach", Sik-Yum Lee. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки,. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Structural Equation Modeling: A Bayesian Approach" для чтения в офлайн-режиме.

Structural Equation Modeling book. Goodreads helps you keep track of books you want to read. Start by marking Structural Equation Modeling: A Bayesian Approach as Want to Read: Want to Read savin. ant to Read.

бесплатно, без регистрации и без смс. 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.

Sik-Yum Lee. Bayesian methods are moving into structural equation modeling. The most sophisticated approach to modeling interactions is Bayesian. People who want to be able to predict the values of observed variables need a Bayesian approach

Sik-Yum Lee. People who want to be able to predict the values of observed variables need a Bayesian approach.

He describes standard structural equation models, such as exploratory factor analysis and the Bentler-Weeks model, examines covariance structure analysis, then presents the Bayesian approach.

A Bayesian Approach in Structural Equation Modeling for Human Fertility in Manipur. Sik-Yum Lee. Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information on parameter is incorporated in the analysis

A Bayesian Approach in Structural Equation Modeling for Human Fertility in Manipur. Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information on parameter is incorporated in the analysis. An iterative algorithm is developed to obtain the Bayes estimates.

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

Structural equation modeling (SEM) is a powerful multivariatemethod allowing the evaluation of a series of simultaneoushypotheses about the impacts of latent and manifest variables onother variables, taking measurement errors into account. As SEMshave grown in popularity in recent years, new models andstatistical methods have been developed for more accurate analysisof more complex data. A Bayesian approach to SEMs allows the use ofprior information resulting in improved parameter estimates, latentvariable estimates, and statistics for model comparison, as well asoffering more reliable results for smaller samples.

Structural Equation Modeling introduces the Bayesianapproach to SEMs, including the selection of prior distributionsand data augmentation, and offers an overview of thesubject’s recent advances.

Demonstrates how to utilize powerful statistical computingtools, including the Gibbs sampler, the Metropolis-Hastingalgorithm, bridge sampling and path sampling to obtain the Bayesianresults.Discusses the Bayes factor and Deviance Information Criterion(DIC) for model comparison.Includes coverage of complex models, including SEMs withordered categorical variables, and dichotomous variables, nonlinearSEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs withmissing data, SEMs with variables from an exponential family ofdistributions, and some of their combinations.Illustrates the methodology through simulation studies andexamples with real data from business management, education,psychology, public health and sociology.Demonstrates the application of the freely available softwareWinBUGS via a supplementary website featuring computer code anddata sets.

Structural Equation Modeling: A Bayesian Approach is amulti-disciplinary text ideal for researchers and students in manyareas, including: statistics, biostatistics, business, education,medicine, psychology, public health and social science.

Comments: (2)
Juce
Bayesian methods are moving into structural equation modeling. The most sophisticated approach to modeling interactions is Bayesian. People who want to be able to predict the values of observed variables need a Bayesian approach.

This book, with the code and datasets available from the publisher's website, will help you to estimate SE models using the Bayesian approach and the free WinBUGS software. Yes, it's a math-heavy book, but Sik-Yum Lee does a great job explaining this very different approach. Lee demonstrates Bayesian methods applied to basic models, interaction models, mixture models, multi-level models, and models with non-normal distributions. You really want to have this book, if you are a serious SEM user.
Bynelad
I would rather not recommend this book to whom is looking for SEM. This book is more like Math oriented..so it is difficult to mention that it is good for students seeking for answeres from the business or sociological perspectives.
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