Abstract
Self-similar processes have recently received increasing attention in the signal processing community, due to their wide applicability in modeling natural phenomena which exhibit “1/ f ” spectra and/or long-range dependence. At the same time, wavelet decomposition has become a very useful tool in describing nonstationary self-similar processes. In this paper, we consider extensions of existing results to non-Gaussian self-similar processes. We first investigate the existence and properties of higher-order statistics of wavelet decomposition for self-similar processes with finite variance. We then consider certain self-similar processes with infinite variance, and study the statistical properties of their wavelet coefficients.
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