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Recent interest on the wavelet transform of digital random signals with long-memory is significantly due to the approximate decorrelation of their wavelet coefficients, which simplifies system identification and estimation. In this paper, we show that for a fairly general model of long-memory across-scale autocovariances of wavelet coefficients converge(More)
OBJECTIVES This paper aims to propose an estimation procedure for the parameters of a generalized fractional process, a fairly general model of long-memory applicable in modeling biomedical signals whose autocorrelations exhibit hyperbolic decay. METHODS We derive a wavelet-based weighted least squares estimator of the long-memory parameter based on the(More)
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