Basic and Advanced Bayesian Structural Equation Modeling: by Sik-Yum Lee, Xin-Yuan Song

By Sik-Yum Lee, Xin-Yuan Song

This booklet offers transparent directions to researchers on the right way to observe Structural Equation types (SEMs) for studying the inter relationships among saw and latent variables.

Basic and complicated Bayesian Structural Equation Modeling introduces uncomplicated and complicated SEMs for studying several types of advanced information, equivalent to ordered and unordered express info, multilevel information, combination info, longitudinal info, hugely non-normal facts, in addition to a few of their mixtures. furthermore, Bayesian semiparametric SEMs to seize the genuine distribution of explanatory latent variables are brought, while SEM with a nonparametric structural equation to evaluate unspecified practical relationships between latent variables also are explored.

Statistical methodologies are constructed utilizing the Bayesian process giving trustworthy effects for small samples and permitting using previous info resulting in higher statistical effects. Estimates of the parameters and version comparability information are got through strong Markov Chain Monte Carlo equipment in statistical computing.

  • Introduces the Bayesian method of SEMs, together with dialogue at the number of past distributions, and information augmentation.
  • Demonstrates how one can make the most of the hot robust instruments in statistical computing together with, yet now not constrained to, the Gibbs sampler, the Metropolis-Hasting set of rules, and direction sampling for generating a variety of statistical effects equivalent to Bayesian estimates and Bayesian version comparability information within the research of simple and complex SEMs.
  • Discusses the Bayes issue, Deviance details Criterion (DIC), and $L_\nu$-measure for Bayesian version comparison.
  • Introduces a few vital generalizations of SEMs, together with multilevel and mix SEMs, latent curve types and longitudinal SEMs, semiparametric SEMs and people with a variety of forms of discrete facts, and nonparametric structural equations.
  • Illustrates the best way to use the freely to be had software program WinBUGS to provide the results.
  • Provides quite a few genuine examples for illustrating the theoretical techniques and computational techniques which are offered during the book.

Researchers and complex point scholars in facts, biostatistics, public healthiness, company, schooling, psychology and social technology will make the most of this book.

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Additional info for Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

Example text

1998) Interaction and Nonlinear Effects in Structural Equation Modeling. Mahwah, NJ: Lawrence Erlbaum Associates. Shi, J. Q. and Lee, S. Y. (2000) Latent variable models with mixed continuous and polytomous data. Journal of the Royal Statistical Society, Series B, 62, 77–87. Song, X. Y. and Lee, S. Y. (2004) Bayesian analysis of two-level nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 57, 29–52. Song, X. Y. and Lee, S.

Ii) A one-way arrow between two variables indicates a postulated direct influence of one variable on another. A two-way arrow between two variables indicates that these variables may be correlated. (iii) The coefficient associated with each arrow indicates the corresponding parameter. (iv) All direct influences of one variable on another are included in the path diagram. Hence the nonexistence of an arrow between two variables means that these two variables are assumed not directly related. Sometimes two-way arrows between two correlated variables and/or residual errors are not drawn for clarity.

2. Note that the same fixed covariates c1 and c2 appear on both the left- and right-hand sides of the path diagram. Moreover, paths related to the residual errors and correlations among latent variables are not presented. 4 Nonlinear SEMs Nonlinear SEMs are formulated with a measurement equation that is basically the same as in linear SEMs, and a structural equation that is nonlinear in the explanatory latent variables. The theoretical motivation for this generalization is natural; it is similar to the extension of simple regression with latent variables to multiple regression with latent variables.

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