.png){kind=icon}
返回
Variable and subset selection in PLS regression
.png){kind=icon}
PRE
AI
DOI：10.1016/S0169-7439(00)00113-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than obtained by different methods. We also present an approach to orthogonal scatter correction. The procedures and comparisons are applied to industrial data. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword:
variable selection
partial least squares (PLS)
principal component analysis (PCA)
H-principle
stepwise regression
Orthogonal Scatter Correction (OSC)
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:
3.8
论文数:
4.6K
被引数:
1.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
EXOGENOUS VASOPRESSIN INDUCED HYPONATREMIA FOLLOWED BY TRANSIENT DIABETES INSIPIDUS外源性血管加压素诱导的低钠血症，随后出现一过性尿崩症
PREAI
分享收藏分享收藏
没有更多内容
没有更多内容