.png){kind=icon}
返回
Subset selection by Mallows Cp: A mixed integer programming approach
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2014.07.056.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper concerns a method of selecting the best subset of explanatory variables for a linear regression model. Employing Mallows' C-p, as a goodness-of-fit measure, we formulate the subset selection problem as a mixed integer quadratic programming problem. Computational results demonstrate that our method provides the best subset of variables in a few seconds when the number of candidate explanatory variables is less than 30. Furthermore, when handling datasets consisting of a large number of samples, it finds better-quality solutions faster than stepwise regression methods do. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Subset selection
Mixed integer programming
Mallows' C-p
Linear regression model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容