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Overview of High-dimensional Measurement Error Regression Models
Version 1
: Received: 15 June 2023 / Approved: 16 June 2023 / Online: 16 June 2023 (04:40:11 CEST)
A peer-reviewed article of this Preprint also exists.
Luo, J.; Yue, L.; Li, G. Overview of High-Dimensional Measurement Error Regression Models. Mathematics 2023, 11, 3202. Luo, J.; Yue, L.; Li, G. Overview of High-Dimensional Measurement Error Regression Models. Mathematics 2023, 11, 3202.
Abstract
High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained increasing attention 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 objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several 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 for future research.
Keywords
Convex optimization; Estimation; High-dimensional data; Hypothesis testing; Measurement error; Variable selection
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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