Quantile-based PLS-SEM with bag of little bootstraps

Abstract

Recently, quantile-based structural equation model has enjoyed increasing popularity in various fields and applications. As we know, structural equation model consists of structural model and measurement model in total. In our quantile-based measurement model, the observed variables are considered to be manifestations of an underlying construct with changing relationships going from the latent variable to its observed variables at different quantiles. Based on our model, we propose a quantile-based partial least square algorithm with bag of little bootstraps. All of our model and algorithm are compared to our quantile-based partial least square algorithms with traditional bootstrap in simulations, and applied to part of IMD (International Institute for Management Development) World Competitiveness Yearbook datasets.

Keywords:

• Quantile regression
• structural equation model
• partial least square
• bag of little bootstraps

Acknowledgments

The author is very grateful to all of the reviewers for their insightful comments and to the interviewees for participating in our investigation. The author wants to thank his parents, his wife Yujie Liu and his cute babies: QQ and QD.

Disclosure statement

The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Author Declarations

The datasets analyzed during the current study are available in the International Institute for Management Development (IMD) World Competitiveness Yearbook, https://worldcompetitiveness.imd.org/. We use R software for programming.

Additional information

Funding

The author’s work was supported by the National Natural Science Foundation of China (72001197), National Statistical Science Research Project of National Bureau of Statistics (2021LY052), Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business University (SZSK202317), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (16XNH102).