Optimization in locally weighted regression

被引：58

作者：

Centner, V ^{[1
]}

Massart, DL ^{[1
]}

机构：

[1] Free Univ Brussels, ChemoAC, B-1090 Brussels, Belgium

来源：

ANALYTICAL CHEMISTRY | 1998年 / 70卷 / 19期

关键词：

D O I：

10.1021/ac980208r

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

The application of locally weighted regression (LWR) to nonlinear calibration problems and strongly clustered calibration data often yields more reliable predictions than global linear calibration models. This study compares the performance of LWR that uses PCR and PLS regression, the Euclidean and Mahalanobis distance as a distance measure, and the uniform and cubic weighting of calibration objects in local models. Recommendations are given on how to apply LWR to near-infrared data sets without spending too much time in the optimization phase.

引用

页码：4206 / 4211

页数：6

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