Optimization in locally weighted regression

V Centner; D L Massart

doi:10.1021/ac980208r

Optimization in locally weighted regression

Anal Chem. 1998 Oct 1;70(19):4206-11. doi: 10.1021/ac980208r.

Authors

V Centner¹, D L Massart

Affiliation

¹ ChemoAC, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium.

PMID: 21651257
DOI: 10.1021/ac980208r

Abstract

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.