OPTIMIZATION IN LOCALLY WEIGHTED REGRESSION

The application of locally weighted regression (LWR) to nonlinear cali bration 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 th e uniform and cubic weighting of calibration objects in local models. Recommendations are given on how to apply LWR to near-infrared data se ts without spending too much time in the optimization phase.