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Overview of High-Dimensional Measurement Error Regression Models

Author

Listed:
  • Jingxuan Luo

    (School of Statistics, Beijing Normal University, Beijing 100875, China)

  • Lili Yue

    (School of Statistics and Data Science, Nanjing Audit University, Nanjing 211815, China)

  • Gaorong Li

    (School of Statistics, Beijing Normal University, Beijing 100875, China)

Abstract

High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained momentum due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n , it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of the objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several novel statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring in future research.

Suggested Citation

  • Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3202-:d:1199385
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    References listed on IDEAS

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