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Wavelet Packet Transform for Fractional Brownian Motion: Asymptotic Decorrelation and Selection of Best Bases.
- Source :
-
IEEE Transactions on Information Theory . Jul2017, Vol. 63 Issue 7, p4532-4550. 19p. - Publication Year :
- 2017
-
Abstract
- Our first goal in this paper is to investigate stationarization and asymptotic decorrelation for fractional Brownian motion (fBm) using wavelet packet transform. The wavelet packets are generated by the N^\mathrm th -order Daubechies scaling function and wavelet. To decorrelate the wavelet packet coefficients asymptotically, we take two strategies: fix $N$ and let absolute scale difference or absolute difference between time shifts get large; and fix scale level and time shift and let $N$ get large. Our second goal is to present the asymptotic properties of the entropy-like cost functional and denoising cost functional and their impact on the selection of best wavelet packet bases when used for fBm plus or not plus independent Gaussian white noise. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00189448
- Volume :
- 63
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Information Theory
- Publication Type :
- Academic Journal
- Accession number :
- 123685239
- Full Text :
- https://doi.org/10.1109/TIT.2017.2700718