Linear Correction Model for Statistical Inference Analysis

A linear correction model based on joint independent information is proposed to optimize the statistical inference performance in high-dimensional data and small sample scenarios by integrating Fiducial inference and Bayesian posterior prediction methods. The model utilizes multi-source data features to construct a joint independent information framework, combined with an information domain dynamic correction mechanism, significantly improving parameter estimation efficiency and confidence interval coverage. Numerical simulation shows that when the sample size is 30, the posterior prediction method has a coverage rate of 0.927, approaching 95% of the theoretical value, and the coverage probability approaches the ideal level with increasing sample size. Compared with traditional methods, the model exhibits stronger adaptability and stability in high-dimensional noise covariance and dynamic data streams, providing an efficient and robust theoretical tool for statistical inference in complex data environments.

@article{Zhao2025,
  title     = {Linear Correction Model for Statistical Inference Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jing Zhao and Zhijiang Zhang},
  doi       = {10.14569/IJACSA.2025.0160543},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160543}
}