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The estimation process in the Bayesian quantile structural equation modeling approach | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 177، دوره 13، شماره 1، خرداد 2022، صفحه 2137-2149 اصل مقاله (448.31 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5909 | ||
| نویسندگان | ||
| Balsam Mustafa Shafeeq* 1؛ Lekaa Ali Mohamed2 | ||
| 1Technical College of Management, Baghdad, Middle Technical University, Iraq | ||
| 2College of Administration and Economics, Department of Statistics, University of Baghdad, Iraq | ||
| تاریخ دریافت: 10 آذر 1400، تاریخ بازنگری: 15 آذر 1400، تاریخ پذیرش: 07 دی 1400 | ||
| چکیده | ||
| latent variable models define as a wide class of regression models with latent variables that cannot be directly measured, the most important latent variable models are structural equation models. Structural equation modeling (SEM) is a popular multivariate technique for analyzing the interrelationships between latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. In general, structural equation models includes a measurement equation to characterize latent variables through multiple observable variables and a mean regression type structural equation to investigate how the explanatory latent variables affect the outcomes of interest. Despite the importance of the structural equations model, it does not provide an accurate analysis of the relationships between the latent variables. Therefore, the quantile regression method will be presented within the structural equations model to obtain a comprehensive analysis of the latent variables. we apply the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The posterior inference is performed using asymmetric Laplace distribution. The estimation is done using the Markov Chain Monte Carlo technique in Bayesian inference. The simulation was implemented assuming different distributions of the error term for the structural equations model and values for the parameters for a small sample size. The method used showed satisfactorily performs results. | ||
| کلیدواژه‌ها | ||
| Bayesian inference؛ latent variable models؛ structural equations model؛ quantile regression | ||
| مراجع | ||
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