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September 27, 2025MathematicsOpen Access

Bayesian Analysis of Nonlinear Quantile Structural Equation Model with Possible Non-Ignorable Missingness

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Authors

LZLu ZhangMTMulati Tuerde

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Overview

This model examines latent variables with non-ignorable missing data, indicating improved analytical precision.

Key Points

  • The proposed model effectively handles complex data and missing data, enhancing latent variable analysis.
  • Simulation studies show that the method excels at different sample sizes and missing data rates.
  • Bayesian techniques like Gibbs and Metropolis–Hastings sampling are utilized for parameter estimation.
  • A case study demonstrates the model's practical application in analyzing company growth and related fields.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68d7cc66eebfec0fc52387d8https://doi.org/10.3390/math13193094
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