Analysis of generalized nonlinear structural equation models by using Bayesian approach with application

Thanoon Y. Thanoon, Robiah Adnan

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)17-45
Number of pages29
JournalPakistan Journal of Statistics and Operation Research
Volume13
Issue number1
StatePublished - 2017

Fingerprint

Gaussian distribution
Normal distribution
Structural equation model
Continuous distributions
Nonlinear equations
Ordered categorical data
WinBUGS
Categorical variable
Model comparison
Gibbs sampling
Sampling methods
Bayesian analysis
Standard error
Statistical inference
Bayesian approach
Testing
Model
Sampling
Scanning electron microscopy

Keywords

  • Bayesian analysis
  • Censored normal distribution
  • Gibbs sampling
  • Ordered categorical variables
  • Structural equation models

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

Cite this

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AB - 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.

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