Fryzlewicz, Piotr 
ORCID: 0000-0002-9676-902X 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
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 |
| Divisions: | Statistics |
| Subjects: | H Social Sciences > HA Statistics |
| Date Deposited: | 20 Dec 2010 10:48 |
| Last Modified: | 11 Dec 2024 17:06 |
| URI: | http://eprints.lse.ac.uk/id/eprint/30976 |
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