  By using chaos expansion into multiple stochastic integrals, we make a
wavelet analysis of two self-similar stochastic processes: the fractional
Brownian motion and the Rosenblatt process. We study the asymptotic behavior of
the statistic based on the wavelet coefficients of these processes. Basically,
when applied to a non-Gaussian process (such as the Rosenblatt process) this
statistic satisfies a non-central limit theorem even when we increase the
number of vanishing moments of the wavelet function. We apply our limit
theorems to construct estimators for the self-similarity index and we
illustrate our results by simulations.
