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A data-driven HAAR-FISZ transform for multiscale variance stabilization

Fryzlewicz, Piotr and Delouille, V (2006) A data-driven HAAR-FISZ transform for multiscale variance stabilization. In: Proceedings of the 13th IEEE/Sp Workshop on Statistical Signal Processing. IEEE, California, USA, pp. 539-544. ISBN 0780394038

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Abstract

We propose a data-driven Haar Fisz transform (DDHFT): a fast, fully automatic, multiscale technique for approximately Gaussianising and stabilizing the variance of sequences of non-negative independent random variables whose variance is a non-decreasing (but otherwise unknown) function of the mean. We demonstrate the excellent performance of the DDHFT on Poisson data. We then use the DDHFT to denoise a solar irradiance time series recorded by the X-ray radiometer on board the GOES satellite: as the noise distribution is unknown, we first take the DDHFT, then use a standard wavelet technique for homogeneous Gaussian data, and then take the inverse DDHFT. The procedure is shown to significantly outperform its competitors

Item Type: Book Section
Additional Information: © 2005 The Authors
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 20 Dec 2010 10:48
URL: http://eprints.lse.ac.uk/30976/

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