Wavelet based estimators for self-similar /spl alpha/-stable processes
The aim of this work is to propose wavelet based estimators for the self-similarity and stability parameters of stable self-similar processes. These estimators are simple both conceptually and practically, consisting of linear fits performed on the wavelet coefficients of stable self-similar processes. Their statistical performance are studied both theoretically, from the analysis of the statistical properties of the wavelet coefficients, and using numerical simulations.