Methods that are robust to outlier values are highlighted. Introduction Classical statistical techniques usually lack the ability to reject outliers which appear in real data, resulting from measurement errors, mixed distributions, noise, etc.
Different methods offer inherent robust properties in the presence of contamination.
The application of robust methods to the domain of spectral data analysis is reviewed in this paper. A short introduction to robust regression by Least-Median of Squares is presented in Section 2. This algorithm is applied to the determination of echelle dispersion relations. Wavelet transform can be useful in complementing robust techniques for multiresolution analysis, and Section 4 presents an application of the Mexican hat transform to the detection of spectral features. A complete trial would require evaluations which would rapidly become impracticable for large n and p values.