Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data

Thanoon Y. Thanoon, Robiah Adnan

Abstract


In this paper, ordered categorical variables are used to compare between linear and nonlinear interactions of fixed covariate and latent variables Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution) is used to handle the problem of ordered categorical data. Statistical inferences, which involve estimation of parameters and their standard deviations, and residuals analyses for testing the selected model, are discussed. The proposed procedure is illustrated by a simulation data obtained from R program. Analysis are done by using OpenBUGS program.

Keywords


Structural equation models, Bayesian analysis, latent variables, Gibbs sampling, ordered categorical data.

Full Text:

PDF


DOI: http://dx.doi.org/10.18187/pjsor.v12i1.952

Refbacks

  • There are currently no refbacks.




Copyright (c) 2016 Pakistan Journal of Statistics and Operation Research

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Title

Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data

Keywords

Structural equation models, Bayesian analysis, latent variables, Gibbs sampling, ordered categorical data.

Description

In this paper, ordered categorical variables are used to compare between linear and nonlinear interactions of fixed covariate and latent variables Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution) is used to handle the problem of ordered categorical data. Statistical inferences, which involve estimation of parameters and their standard deviations, and residuals analyses for testing the selected model, are discussed. The proposed procedure is illustrated by a simulation data obtained from R program. Analysis are done by using OpenBUGS program.

Date

2016-03-02

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 12 No. 1, 2016



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810