Comparison of two recently proposed expressions for partial least squares regression prediction error

被引：32

作者：

Faber, NM

机构：

[1] Dunantsingel 28, 2806 JB, Gouda

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2000年 / 52卷 / 02期

关键词：

PLS; PCR; prediction error; prediction interval; variance; bias; errors-in-variables; unscrambler; Monte Carlo simulation;

D O I：

10.1016/S0169-7439(00)00076-9

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Two recently proposed expressions for partial least squares (PLS) prediction error are compared. Using extensive Monte Carlo simulations, it is found. that the expression based on the so-called errors-in-variables approach yields prediction intervals with coverage probabilities close to their nominal value, whereas the expression, which is implemented in the latest version of Unscrambler (7.0), is found to behave unsatisfactorily. The difference between the two approaches is illustrated on a real near-infrared data set taken from the literature. (C) 2000 Elsevier Science B.V. All rights reserved.

引用

页码：123 / 134

页数：12

共 19 条

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