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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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[1] R. Alhamzawi, Model selection in quantile regression models, J. Appl. Stat. 42 (2015), no. 2, 445–458. [2] D.F. Andrews and C.L. Mallows, Scale mixtures of normal distributions, J. Royal Stat. Soc.: Ser. B Method. 36 (1974), no. 1, 99–102. [3] X. Feng, Y. Wang, B. Lub, and X. Song, Bayesian regularized quantile structural equation models, J. Multivariate Anal. 154 (2017), 234–248. [4] R. Koenker and G. Bassett Jr., Regression quantiles, Econ.: J. Econ. Soc. 46 (1978), no. 1, 33–50. [5] I. Komujer, Quasi-maximum likelihood estimation for conditional quantiles, J. Econ. 128 (2005), 137–164. [6] H. Kozumi and G. Kobayashi, Gibbs sampling methods for Bayesian quantile regression, J. Stat. Comput. Simulat. 81 (2011), no. 11, 1565–1578. [7] Q. Li, R. Xi, and N. Lin, Bayesian regularized quantile regression, Bayesian Anal. 5 (2010), 533–556. [8] Y. Li and J. Zhu, L1-norm quantile regression, J. Comput. Graph. Statist. 17 (2008), no. 1, 163–185. [9] T. Park and G. Casella, The Bayesian lasso, J. Amer. Statist. Assoc. 103 (2008), 681–686. [10] X.Y. Song and S.Y. Lee, Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley and Sons, 2012. [11] R. Tibshirani, Regression shrinkage and selection via the lasso, J. Royal Stat. Soc. Ser. B: Statist. Method. 58 (1996), no. 1, 267–288. [12] Y. Wang, X.-N. Feng, and X.-Y. Song, Bayesian quantile structural equation models, Structural Equ. Model.: Multidiscip. J. 23 (2016), no. 2, 246–258. [13] K. Yu and R.A. Moyeed, Bayesian quantile regression, Statist. Prob. Lett. 54 (2001), 437–447. [14] A. Zellner, An Introduction to Bayesian Inference in Econometrics, John Wiley and Sons, New York, 1971. | ||
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