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Smoothing data with correlated noise via Fourier transform

Author

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  • Amato, Umberto
  • De Feis, Italia

Abstract

The problem of smoothing data trough a transform in the Fourier domain is analyzed in the case of correlated noise affecting data. A regularization method and two GCV-type criteria are resorted in order to solve the problem, in analogy with the case of uncorrelated noise. All convergence theorems stated for uncorrelated noise are here generalized to the case of correlated noise. Numerical experiments on significant test functions are shown.

Suggested Citation

  • Amato, Umberto & De Feis, Italia, 2000. "Smoothing data with correlated noise via Fourier transform," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 52(3), pages 175-196.
    • Handle: RePEc:eee:matcom:v:52:y:2000:i:3:p:175-196
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