| 摘要: |
| 本文研究了高维线性模型的变量选择和参数估计问题,
提出了广义SELO惩罚函数族, 推广了SELO惩罚回归方法.
模拟研究和实际数据分析评估了提出方法在有限样本下的表现. |
| 关键词: 坐标下降 高维BIC 局部线性逼近 惩罚最小二乘 SELO惩罚 |
| DOI： |
| 分类号:O212.1 |
| 基金项目:国家自然科学基金(11501579; 41572315);中国地质大学(武汉)中央高校基本科研业务费专项资金(CUGW150809) |
|
| High-dimensional variable selection via generalized SELO-penalized linear regression |
|
SHI YUE YONG1, JIAO YU LING2
|
|
1.School of Economics and Management, China University of Geosciences (Wuhan);2.School of Statistics and Mathematics, Zhongnan University of Economics and Law
|
| Abstract: |
| This paper studies the problem of variable selection and parameter estimation
in high-dimensional linear models,
presents a generalized SELO penalty function family
and extends the SELO-penalized regression method to a more general framework.
Simulation studies and a real data analysis are conducted to assess the finite sample performance of the proposed method. |
| Key words: coordinate descent high-dimensional BIC local linear approximation penalized least squares SELO penalty |
