Maximum likelihood estimators and random walks in long memory models
Statistics Theory
2009-12-19 v2 Probability
Statistics Theory
Abstract
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.
Cite
@article{arxiv.0711.0513,
title = {Maximum likelihood estimators and random walks in long memory models},
author = {Karine Bertin and Soledad Torres and Ciprian Tudor},
journal= {arXiv preprint arXiv:0711.0513},
year = {2009}
}
Comments
To appear in "Statistics"