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Orthogonality Conditions for Non-Dyadic Wavelet Analysis

Author

Listed:
  • Stephen Pollock

    (Queen Mary, University of London)

  • Iolanda Lo Cascio

    (Queen Mary, University of London)

Abstract

The conventional dyadic multiresolution analysis constructs a succession of frequency intervals in the form of (π / 2 j, π / 2 j - 1); j = 1, 2, . . . , 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. A non-dyadic mixed-radix wavelet analysis is described that 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.

Suggested Citation

  • Stephen Pollock & Iolanda Lo Cascio, 2005. "Orthogonality Conditions for Non-Dyadic Wavelet Analysis," Working Papers 529, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:529
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2005/items/wp529.pdf
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    References listed on IDEAS

    as
    1. Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
    2. Nason, G.P. & von Sachs, R., 1999. "Wavelets in Time Series Analysis," Papers 9901, Catholique de Louvain - Institut de statistique.
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    More about this item

    Keywords

    Wavelets; Non-dyadic analysis; Fourier analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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