A Class of Fast Methods for Processing Irregularly Sampled or Otherwise   Inhomogeneous One-Dimensional Data
  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 $O(n^3)$ to $O(n)$ operations, where $n$ 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. 
George B. Rybicki and William H. Press (Harvard-Smithsonian Center for   Astrophysics)