Estimation of partial least squares regression prediction uncertainty when the reference values carry a sizeable measurement error

被引：0
|
作者：
Pierna, JAF
Jin, L
Wahl, F
Faber, NM
Massart, DL
机构：
[1] Free Univ Brussels, ChemoAC, Inst Pharmaceut, B-1090 Brussels, Belgium
[2] Nanchang Univ, Inst Appl Chem, Nanchang 330047, Peoples R China
[3] Inst Francais Petr, F-69390 Vernaison, France
[4] DLO, ATO, Dept Prod & Control Syst, NL-6700 AA Wageningen, Netherlands
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2003年 / 65卷 / 02期
关键词：
multivariate calibration; partial least squares regression; uncertainty estimation; standard error of prediction; Monte Carlo simulation; bootstrap; noise addition; near-infrared spectroscopy;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The prediction uncertainty is studied when using a multivariate partial least squares regression (PLSR) model constructed with reference values that contain a sizeable measurement error. Several approximate expressions for calculating a sample-specific standard error of prediction have been proposed in the literature. In addition, Monte Carlo simulation methods such as the bootstrap and the noise addition method can give an estimate of this uncertainty. In this paper, two approximate expressions are compared with the simulation methods for three near-infrared data sets. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：281 / 291
页数：11
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