Results 141 to 150 of about 121,610 (166)

Bayesian Model Comparison of Structural Equation Models

2008
Structural equation modeling is a multivariate method for establishing meaningful models to investigate the relationships of some latent (causal) and manifest (control) variables with other variables. In the past quarter of a century, it has drawn a great deal of attention in psychometrics and sociometrics, both in terms of theoretical developments and
Sik-Yum Lee, Xin-Yuan Song
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Bayesian diagnostics of transformation structural equation models

Computational Statistics & Data Analysis, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Ji, Liu, Pengfei, Song, Xinyuan
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Bayesian Analysis of Structural Equation Modeling

2002
A Bayesian procedure to make exact distributional inferences about all structural parameters and latent variables was proposed. This procedure handles the problem associated with the fixed parameters by means of conditinalization, and uses the Gibbs sampler to derive the posterior distribution for each unknown quantitiy.
Kazuo Shigemasu, Takahiro Hoshino, Takuya Ohmori   +2 more
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Bayesian analysis of structural equation models with dichotomous variables

Statistics in Medicine, 2003
AbstractStructural equation modelling has been used extensively in the behavioural and social sciences for studying interrelationships among manifest and latent variables. Recently, its uses have been well recognized in medical research. This paper introduces a Bayesian approach to analysing general structural equation models with dichotomous variables.
Sik-Yum, Lee, Xin-Yuan, Song
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Bayesian Structural Equation Modeling in Sport and Exercise Psychology

Journal of Sport and Exercise Psychology, 2015
Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a
Andreas, Stenling, Andreas, Ivarsson, Urban, Johnson, Magnus, Lindwall   +3 more
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Bayesian structural equation modeling method for hierarchical model validation

Reliability Engineering & System Safety, 2009
A building block approach to model validation may proceed through various levels, such as material to component to subsystem to system, comparing model predictions with experimental observations at each level. Usually, experimental data becomes scarce as one proceeds from lower to higher levels.
Xiaomo Jiang, Sankaran Mahadevan
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Bayesian structural equation modeling for the health index

Journal of Applied Statistics, 2013
There are many factors which could influence the level of health of an individual. These factors are interactive and their overall effects on health are usually measured by an index which is called as health index. The health index could also be used as an indicator to describe the health level of a community.
Ferra Yanuar, Kamarulzaman Ibrahim, Abdul Aziz Jemain   +2 more
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Bayesian hierarchical uncertainty quantification by structural equation modeling

International Journal for Numerical Methods in Engineering, 2009
AbstractUncertainty quantification is playing an increasingly important role in assessing the performance, safety, and reliability of complex physical systems in the absence of adequate amount of experimental data. Simulation of a complex system involves multiple levels of modeling, such as material (lowest level) to component to subsystem to system ...
Jiang, Xiaomo, Mahadevan, Sankaran
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Bayesian Nonlinear Structural Equation Modeling

2015
Structural Equation Modeling (SEM) is a multivariate method that incorporates ideas from regression, path analysis and factor analysis. SEM has been widely applied in examine inter-relationships among latent and observed variables in social, psychological, and medical research.
Altındağ, İlkay, Genç, Aşır
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Bayesian local influence of semiparametric structural equation models

Computational Statistics & Data Analysis, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ming Ouyang, Xiaodong Yan, Ji Chen, Niansheng Tang, Xinyuan Song   +4 more
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