An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model

被引：42
作者：
Gourvénec, S
Pierna, JAF
Massart, DL
Rutledge, DN
机构：
[1] Free Univ Brussels, Inst Pharmaceut, ChemoAC, B-1090 Brussels, Belgium
[2] Inst Natl Agron Paris Grignon, Chim Analyt Lab, F-75005 Paris, France
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2003年 / 68卷 / 1-2期
关键词：
PLS; complexity; Monte Carlo Cross-Validation; smoothing; Durbin-Watson criterion; adjusted Wold's R criterion;
D O I：
10.1016/S0169-7439(03)00086-8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A crucial point of the PLS algorithm is the selection of the right number of factors or components (i.e., the determination of the optimal complexity of the system to avoid overfitting). The leave-one-out cross-validation is usually used to determine the optimal complexity of a PLS model, but in practice, it is found that often too many components are retained with this method. In this study, the Monte Carlo Cross-Validation (MCCV) and the PoLiSh smoothed regression are used and compared with the better known adjusted Wold's R criterion. (C) 2003 Elsevier B.V. All rights reserved.
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收藏
页码：41 / 51
页数：11
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