Self-Similarity Parameter Estimation for k-dimensional Processes

摘要

An algorithm is proposed that allows to estimate the self-similarity parameter of a fractal k-dimensional stochastic process. Our technique greatly improves the processing times of a distribution-based estimator, that — introduced years ago — efficiently worked only in the one-dimensional distribution case.