基于Geman-McClure损失的稳健变量选择
Robust Variable Selection Based on Geman-McClure Loss
DOI: 10.12677/SA.2023.122042, PDF,    科研立项经费支持
作者: 谢传琪, 王延新*：宁波工程学院理学院，浙江 宁波
关键词: 稳健变量选择；高维数据；Adaptive LASSO；Geman-McClure损失；Robust Variable Selection； High-Dimensional Data； Adaptive LASSO； Geman-McClure Loss
摘要: 基于惩罚函数的最小二乘估计或似然估计是变量选择的有效方法。但当数据存在异常值时，罚最小二乘或似然估计的稳健性受到极大挑战。本文提出基于Geman-McClure损失的稳健变量选择方法，该损失函数能够有效抵制数据中异常值的影响。数值模拟和实际数据分析验证了该模型的有效性和稳健性。
Abstract: Least squares or likelihood estimation based on penalty function is an effective method for variable selection. However, the robustness of penalized least squares or likelihood estimation is greatly challenged when there are outliers in the data. In this paper, we propose a robust variable selection method based on the Geman-McClure loss, which is an effective loss function to counteract the influence of outliers in the data. Numerical simulations and analysis of real data validate the validity and robustness of the model.
文章引用：谢传琪, 王延新. 基于Geman-McClure损失的稳健变量选择[J]. 统计学与应用, 2023, 12(2): 391-397. https://doi.org/10.12677/SA.2023.122042

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