Quantifying the effect of measurement errors on the uncertainty in bilinear model predictions: a small simulation study

被引:13
作者
Faber, NM [1 ]
机构
[1] ATO, Dept Prod & Control Syst, NL-6700 AA Wageningen, Netherlands
关键词
bilinear calibration; error estimation; Monte Carlo simulation; resampling; bootstrap; jack-knife;
D O I
10.1016/S0003-2670(01)00872-8
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Four methods are compared for quantifying the effect of measurement errors on the uncertainty in bilinear model predictions. These methods amount to (1) evaluating an approximate expression for prediction variance, (2) bootstrapping residuals left after fitting the data matrices using a singular value decomposition, (3) adding noise from an appropriate distribution to the original data, and (4) jack-knifing rows and columns of the data matrices. The comparison is carried out for liquid chromatography/ultraviolet data obtained from Malinowski and the models are constructed using the generalized rank annihilation method. It is found that the first three methods give highly consistent results, whereas the jack-knife yields uncertainty estimates that have no clear interpretation. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:193 / 201
页数:9
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