paper

A Class of Fast Methods for Processing Irregularly Sampled or Otherwise Inhomogeneous One-Dimensional Data

arXiv:comp-gas/9405004 · doi:10.1103/PhysRevLett.74.1060

Abstract

With the ansatz that a data set's correlation matrix has a certain parametrized form (one general enough, however, to allow the arbitrary specification of a slowly-varying decorrelation distance and population variance) the general machinery of Wiener or optimal filtering can be reduced from to operations, where is the size of the data set. The implied vast increases in computational speed can allow many common sub-optimal or heuristic data analysis methods to be replaced by fast, relatively sophisticated, statistical algorithms. Three examples are given: data rectification, high- or low- pass filtering, and linear least squares fitting to a model with unaligned data points.

7 pages, LaTeX with REVTeX 3.0 macros, no figures. A toolkit with implementations (in Fortran 90) of the algorithms is available by anonymous ftp to cfata4.harvard.edu

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