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Open AccessArticle
Inverse Probability-Weighted Estimation for Dynamic Structural Equation Model with Missing Data
by
Hao Cheng
Hao Cheng
National Academy of Innovation Strategy, China Association for Science and Technology, Beijing 100038, China
Mathematics 2024, 12(19), 3010; https://doi.org/10.3390/math12193010 (registering DOI)
Submission received: 3 September 2024
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Revised: 24 September 2024
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Accepted: 25 September 2024
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Published: 26 September 2024
Abstract
In various applications, observed variables are missing some information that was intended to be collected. The estimations of both loading and path coefficients could be biased when ignoring the missing data. Inverse probability weighting (IPW) is one of the well-known methods helping to reduce bias in regressions, while belonging to a promising but new category in structural equation models. The paper proposes both parametric and nonparametric IPW estimation methods for dynamic structural equation models, in which both loading and path coefficients are developed into functions of a random variable and of the quantile level. To improve the computational efficiency, modified parametric IPW and modified nonparametric IPW are developed through reducing inverse probability computations but making fuller use of completely observed information. All the above IPW estimation methods are compared to existing complete case analysis through simulation investigations. Finally, the paper illustrates the proposed model and estimation methods by an empirical study on digital new-quality productivity.
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MDPI and ACS Style
Cheng, H.
Inverse Probability-Weighted Estimation for Dynamic Structural Equation Model with Missing Data. Mathematics 2024, 12, 3010.
https://doi.org/10.3390/math12193010
AMA Style
Cheng H.
Inverse Probability-Weighted Estimation for Dynamic Structural Equation Model with Missing Data. Mathematics. 2024; 12(19):3010.
https://doi.org/10.3390/math12193010
Chicago/Turabian Style
Cheng, Hao.
2024. "Inverse Probability-Weighted Estimation for Dynamic Structural Equation Model with Missing Data" Mathematics 12, no. 19: 3010.
https://doi.org/10.3390/math12193010
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