A linearization method for partial least squares regression prediction uncertainty

被引：6
|
作者：
Zhang, Ying [1 ]
Fearn, Tom [1 ]
机构：
[1] UCL, Dept Stat Sci, London WC1E 6BT, England
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2015年 / 140卷
关键词：
Multivariate calibration; Partial least squares regression; Mean squared prediction error; Linearization parametric bootstrap; Parametric bootstrap; INTERVALS; MATRIX;
D O I：
10.1016/j.chemolab.2014.11.011
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We study a local linearization approach put forward by Romera to provide an approximate variance for predictions in partial least squares regression. We note and correct some problems with the original formulae, study the stability of the resulting approximation using some simulations, and suggest an alternative method of computation using a parametric bootstrap. The alternative method is more stable than the algebraic approximation and is faster when the number of predictors is large. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：133 / 140
页数：8
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