Maximum likelihood estimators and random walks in long memory models
نویسندگان
چکیده
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 in the non-Gaussian case 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. 2000 AMS Classification Numbers: 60G18, 62M99.
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تاریخ انتشار 2008