The Bayesian Lasso of quantile structural equation model

Shafeeq, Balsam Mustafa; Muhamed, Lekaa Ali

doi:10.22075/ijnaa.2025.9624

The Bayesian Lasso of quantile structural equation model

Document Type : Research Paper

Authors

Balsam Mustafa Shafeeq ¹

Lekaa Ali Muhamed ²

¹ Technical College of Management, Middle Technical University, Baghdad, Iraq

² College of Administration and Economics, Department of Statistics, University of Baghdad, Baghdad, Iraq

10.22075/ijnaa.2025.9624

Abstract

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.

Keywords

Bayesian inference

Lasso regression

quantile structural equation model

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