基于非凸惩罚回归的参数估计和异常值检测
Parameter Estimation and Outliers Detection Based on Nonconvex Penalized Regression
摘要:本文基于最优化理论提出了一种实现多元线性回归模型的参数估计和异常值检测的方法。首先，建立了基于Huber损失函数和l 0惩罚项的回归模型，为便于求解进一步将该模型中l 0惩罚项松弛为Capped-l 1惩罚；其次，刻画了松弛问题的方向稳定点，并建立了原问题和松弛问题的等价性。 最后提出了松弛问题的光滑化模型，并证明了光滑模型与松弛问题稳定点的一致性。
Abstract:This paper presents a method to implement parameter estimation and outliers de- tection for multiple linear regression models based on optimization theory. First, a regression model based on the Huber loss function and the l 0penalty term is devel- oped, and the l 0penalty term in this model is further relaxed to the Capped-l 1penalty to facilitate the solution; and second, the directional stability point of the relaxation problem is characterized, and the equivalence of the original and relaxation problems is established. Finally, we propose a smooth model for the relaxation problem and prove the consistency of the stable point of smooth model and the relaxation problem.
文章引用：张尊皓, 彭定涛, 苏妍妍. 基于非凸惩罚回归的参数估计和异常值检测[J]. 应用数学进展, 2022, 11(12): 9081-9095. https://doi.org/10.12677/AAM.2022.1112958

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