Estimation of self-similarity index for Rosenblatt models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10501393" target="_blank" >RIV/00216208:11320/24:10501393 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CDC56724.2024.10886859" target="_blank" >https://doi.org/10.1109/CDC56724.2024.10886859</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CDC56724.2024.10886859" target="_blank" >10.1109/CDC56724.2024.10886859</a>

Result language
angličtina
Original language name
Estimation of self-similarity index for Rosenblatt models
Original language description
In this paper, we address the self-similarity index estimation problem. For observations of a Rosenblatt process, we propose a new estimator based on the logarithmic variation with adjustment for bias and perform numerical simulations that show good performance of this estimator. We also address the problem of estimation of the self-similarity index for SDEs driven by a Rosenblatt process. A robust (against drift and noise intensity misspecification) estimator is described and its (asymptotic) behavior is analyzed both theoretically and in simulations.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA22-12790S" target="_blank" >GA22-12790S: Stochastic systems in infinite dimensions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the IEEE Conference on Decision and Control
ISBN
979-8-3503-1633-9
ISSN
0743-1546
e-ISSN
2576-2370
Number of pages
6
Pages from-to
4227-4232
Publisher name
IEEE
Place of publication
New York, NY
Event location
Milan, Italy
Event date
Dec 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001445827203100

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