Critical evaluation of a significance test for partial least squares regression

被引：8
|
作者：
Faber, NM
机构：
[1] 2806 JB Gouda
[2] ATO, Department of Production and Control System, 6700 AA Wageningen
来源：
ANALYTICA CHIMICA ACTA | 2001年 / 432卷 / 02期
关键词：
multivariate calibration; partial least squares regression; principal component regression; significance test;
D O I：
10.1016/S0003-2670(00)01381-7
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
A significance test for determining the optimum model dimension for partial least squares regression is critically examined. It is derived that this significance test is equivalent with a well-known t-test for principal component regression. The results for a near-infrared data set suggest that the conventional methods of optimising the prediction error estimate obtained from a test set or cross-validation are to be preferred. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：235 / 240
页数：6
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