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What is Least-Squares Smoothing for Nonuniform Data about?

This document describes a method for smoothing and differentiating non-uniformly spaced data using a convolution method. The method extends the Savitzky-Golay technique, which was previously only applicable to uniformly spaced data, to include non-uniformly spaced data points. It does this by using orthogonal polynomials to calculate least-squares convolution weights for fitting a polynomial to local data points. This allows smoothed or differentiated values to be calculated for all data points, avoiding da

Author

Vladimir Pelekhaty

Language

EN