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One-step estimation for the fractional Gaussian noise at high-frequency

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03022878
Contributeur : Edp Sciences <>
Soumis le : mercredi 25 novembre 2020 - 06:29:02
Dernière modification le : jeudi 26 novembre 2020 - 03:30:37

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Alexandre Brouste, Marius Soltane, Irene Votsi. One-step estimation for the fractional Gaussian noise at high-frequency. ESAIM: Probability and Statistics, EDP Sciences, 2020, 24, pp.827-841. ⟨10.1051/ps/2020022⟩. ⟨hal-03022878⟩

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