  We consider statistical models driven by Gaussian and non-Gaussian
self-similar processes with long memory and we construct maximum likelihood
estimators (MLE) for the drift parameter. Our approach is based on the
approximation by random walks of the driving noise. We study the asymptotic
behavior of the estimators and we give some numerical simulations to illustrate
our results.
