Overview of High-Dimensional Measurement Error Regression Models

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