The conventional dyadic multiresolution analysis constructs a succession of frequency intervals in the form of $(\pi/2^j, \pi/2^{j-1});j = 1, 2, \ldots, n$ of which the bandwidths are halved repeatedly in the descent from high frequencies to low frequencies. Whereas this scheme provides an excellent framework for encoding and transmitting signals with a high degree of data compression, it is less appropriate to the purposes of statistical data analysis. This paper describes a non-dyadic mixed-radix wavelet analysis which allows the wave bands to be defined more flexibly than in the case of a conventional dyadic analysis. The wavelets that form the basis vectors for the wave bands are derived from the Fourier transforms of a variety of functions that specify the frequency responses of the filters corresponding to the sequences of wavelet coefficients.
Pollock, D., Lo Cascio, I. (2006). Non-Dyadic Wavelet Analysis. In Kontoghiorghes E.J., Gatu C.. (a cura di), Optimisation, Econometric and Financial Analysis. (pp. 169-204). Springer.
Non-Dyadic Wavelet Analysis
LO CASCIO, Iolanda
2006-01-01
Abstract
The conventional dyadic multiresolution analysis constructs a succession of frequency intervals in the form of $(\pi/2^j, \pi/2^{j-1});j = 1, 2, \ldots, n$ of which the bandwidths are halved repeatedly in the descent from high frequencies to low frequencies. Whereas this scheme provides an excellent framework for encoding and transmitting signals with a high degree of data compression, it is less appropriate to the purposes of statistical data analysis. This paper describes a non-dyadic mixed-radix wavelet analysis which allows the wave bands to be defined more flexibly than in the case of a conventional dyadic analysis. The wavelets that form the basis vectors for the wave bands are derived from the Fourier transforms of a variety of functions that specify the frequency responses of the filters corresponding to the sequences of wavelet coefficients.File | Dimensione | Formato | |
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