In this paper, Bayesian analysis is used in nonlinear structural equation models with two population of data and the Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of ordered categorical data in Bayesian multiple group SEMs and compared with the method that treats ordered categorical variables as a continuous normal distribution. Statistical inferences, which involve the estimation of parameters and their standard errors, and residuals analyses for testing the posited model are discussed. The proposed procedure is illustrated using real data with the results obtained from the WinBUGS program
AbstractNon-linear structural equation models are widely used to analyze the relationships among out...
Non-linear structural equation models are widely used to analyze the relationships among outcomes an...
The purpose of this paper is to develop a latent variable model with nonlinear covariates and latent...
In this paper ordered categorical variables are used in Bayesian multiple group nonlinear structural...
In this paper, Bayesian analysis is used in nonlinear structural equation models with two population...
This paper is designed to give a complete overview of the literature that is available, as it relate...
In this paper, ordered categorical variables are used to compare between linear and nonlinear intera...
In this paper, ordered categorical variables are used to compare between linear and nonlinear Bayesi...
In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian str...
In this paper, ordered categorical variables are used to compare between linear and nonlinear Bayesi...
The purpose of this paper is to describe the mixed variables (ordered categorical and dichotomous) i...
This thesis describes the parameter estimation of structural equation models with ordered categorica...
The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution ov...
Keywords: Bayesian analysis, Finite mixture, Gibbs sampler, Langevin-Hasting sampler, MH sampler, Mo...
Structural equation modeling (SEM) is a statistical methodology that is commonly used to study the r...
AbstractNon-linear structural equation models are widely used to analyze the relationships among out...
Non-linear structural equation models are widely used to analyze the relationships among outcomes an...
The purpose of this paper is to develop a latent variable model with nonlinear covariates and latent...
In this paper ordered categorical variables are used in Bayesian multiple group nonlinear structural...
In this paper, Bayesian analysis is used in nonlinear structural equation models with two population...
This paper is designed to give a complete overview of the literature that is available, as it relate...
In this paper, ordered categorical variables are used to compare between linear and nonlinear intera...
In this paper, ordered categorical variables are used to compare between linear and nonlinear Bayesi...
In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian str...
In this paper, ordered categorical variables are used to compare between linear and nonlinear Bayesi...
The purpose of this paper is to describe the mixed variables (ordered categorical and dichotomous) i...
This thesis describes the parameter estimation of structural equation models with ordered categorica...
The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution ov...
Keywords: Bayesian analysis, Finite mixture, Gibbs sampler, Langevin-Hasting sampler, MH sampler, Mo...
Structural equation modeling (SEM) is a statistical methodology that is commonly used to study the r...
AbstractNon-linear structural equation models are widely used to analyze the relationships among out...
Non-linear structural equation models are widely used to analyze the relationships among outcomes an...
The purpose of this paper is to develop a latent variable model with nonlinear covariates and latent...