Smoothing data with correlated noise via Fourier transform

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.

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Year of publication:	2000
Authors:	Amato, Umberto ; De Feis, Italia
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 52.2000, 3, p. 175-196
Publisher:	Elsevier
Subject:	Fourier domain \| GCV-type criteria \| Uncorrelated noise

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010749430