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The Bayesian Lasso of quantile structural equation model | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 09 اردیبهشت 1404 اصل مقاله (1.59 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2025.9624 | ||
| نویسندگان | ||
| Balsam Mustafa Shafeeq1؛ Lekaa Ali Muhamed* 2 | ||
| 1Technical College of Management, Middle Technical University, Baghdad, Iraq | ||
| 2College of Administration and Economics, Department of Statistics, University of Baghdad, Baghdad, Iraq | ||
| تاریخ دریافت: 24 آبان 1400، تاریخ بازنگری: 29 آذر 1400، تاریخ پذیرش: 09 بهمن 1400 | ||
| چکیده | ||
| Structural equation models have been extensively applied to medical and social sciences, the most important latent variable models are structural equation models. Structural equation modelling (SEM) is a popular multivariate technique for analyzing the interrelationships between latent variables. In general, structural equation models include 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. In this study, we apply Bayesian least absolute shrinkage and selection operator (Lasso) procedure to conduct estimation in the Quantile SEM, and compare this estimator with estimator of Bayesian Quantile Structural equation model, and apply the use of the Markov chain Monte Carlo (MCMC) method by Gibbs sampler to conduct Bayesian inference. The simulation was implemented assuming different distributions of the error term for the structural equations model and values of the parameters for a small sample size. | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 33 تعداد دریافت فایل اصل مقاله: 21 |
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