Locally-Biased Regression

Abstract

After a brief review of local calibration methods, a new and relatively simple method is proposed. Given a database of calibration samples, a global calibration based on all these samples and an unknown for which we wish to make a prediction, the method selects a subset of the calibration samples judged to be spectrally similar to the unknown and uses these to determine either a skew and bias or a simple bias correction to the global calibration. Spectral similarity is defined in a two-dimensional space, with one axis focussing on similarity with respect to the analyte value to be predicted, and the other on more general spectral similarity. The computations required to make a prediction are simple by the standards of local methods.