山西大学学报（自然科学版）2025，Vol.48Issue(5)：900-910,11.DOI:10.13451/j.sxu.ns.2024025
随机限制回归模型的自适应最小绝对收缩和选择算子方法
Adaptive Least Absolute Shrinkage and Selection Operator Method for Stochastic Limit Regression Model
摘要
Abstract
The Mixed least absolute shrinkage and selection operator(M-Lasso)method can use random prior information at the same time as variable selection,but the minimum absolute shrinkage and selection operator(Lasso)based on this method is equally weighted for each coefficient,which may cause some important information to be overcompressed.To address this issue,this paper proposes the stochastic constrained adaptive Lasso(Ma-Lasso)method.The method assigns different weights to the coefficients and has oracle properties.It uses stochastic prior information along with variable selection,which can improve the precision of the esti-mation.The analysis of numerical experimental results reveals that the method exhibits a smaller mean square error on the sparse model than the other methods,and also has some advantages in terms of the discovery rate,the true discovery rate,and the ratio of the number of times the true model is selected.Finally,by applying Ma-Lasso to the quarterly financial data and stock price data of Kweichow Moutai,it is found that the BIC value of the model constructed by this method decreases by about 5%compared with M-Lasso method,which further verifies its superiority.关键词
稀疏模型/变量选择/均方误差/随机先验信息Key words
sparse model/variable selection/mean squared error/random prior information分类
数理科学引用本文复制引用
靳琴琴,彭伟,廖天自,孙宝..随机限制回归模型的自适应最小绝对收缩和选择算子方法[J].山西大学学报（自然科学版）,2025,48(5):900-910,11.基金项目
山西省高校科技创新项目(2021L324) （2021L324）
山西省基础研究计划青年科学研究项目(202103021223282) （202103021223282）
太原科技大学博士科研启动基金(20212014) （20212014）
