Volume 24, 2020
|Page(s)||827 - 841|
|Published online||24 November 2020|
One-step estimation for the fractional Gaussian noise at high-frequency
Laboratoire Manceau de Mathématiques, Le Mans Université,
Le Mans, France.
* Corresponding author: email@example.com
Accepted: 17 September 2020
The present paper concerns the parametric estimation for the fractional Gaussian noise in a high-frequency observation scheme. The sequence of Le Cam’s one-step maximum likelihood estimators (OSMLE) is studied. This sequence is defined by an initial sequence of quadratic generalized variations-based estimators (QGV) and a single Fisher scoring step. The sequence of OSMLE is proved to be asymptotically efficient as the sequence of maximum likelihood estimators but is much less computationally demanding. It is also advantageous with respect to the QGV which is not variance efficient. Performances of the estimators on finite size observation samples are illustrated by means of Monte-Carlo simulations.
Mathematics Subject Classification: 62F12 / 62M09 / 65U05
Key words: Fractional Gaussian noise / infill asymptotics / efficient estimation / Fisher scoring
© The authors. Published by EDP Sciences, SMAI 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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