.png){kind=icon}
返回
Variable selection in linear regression
.png){kind=icon}
OA
AI
DOI：10.1177/1536867X1101000407.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise methods such as forward selection and backward elimination are provided. The user may specify Mallows's C-p, Akaike's information criterion, Akaike's corrected information criterion, Bayesian information criterion, or R-2 adjusted as the information criterion for the selection. When the user specifies the best subset option, the leaps-and-bounds algorithm (Furnival and Wilson, Technometrics 16: 499-511) is used to determine the best subsets of each predictor size. All the previously mentioned information criteria are reported for each of these subsets. We also provide options for doing variable selection only on certain predictors (as in [R] nestreg) and support for weighted linear regression. All options are demonstrated on real datasets with varying numbers of predictors.
Keyword:
st0213
vselect
variable selection
regress
nestreg
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
S
IF:
2.4
论文数:
1.2K
被引数:
8.4K
机构
引用论文
分享收藏分享收藏Real-Time Analysis of a Modified State Observer for Sensorless Induction Motor Drive Used in Electric Vehicle Applications.png)
EnergiesOAAI
EnergiesIF0
OAAI分享收藏
查看更多内容