Drift estimation for a Lévy-driven Ornstein–Uhlenbeck process with heavy tails

被引:0
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作者
Alexander Gushchin
Ilya Pavlyukevich
Marian Ritsch
机构
[1] Steklov Mathematical Institute of Russian Academy of Sciences,Institute of Mathematics
[2] National Research University Higher School of Economics,undefined
[3] Friedrich Schiller University Jena,undefined
来源
Statistical Inference for Stochastic Processes | 2020年 / 23卷
关键词
Lévy process; Ornstein–Uhlenbeck type process; Local asymptotic mixed normality; Heavy tails; Regular variation; Maximum likelihood estimator; Asymptotic observed information; 62M05; 60F05; 60J75;
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摘要
We consider the problem of estimation of the drift parameter of an ergodic Ornstein–Uhlenbeck type process driven by a Lévy process with heavy tails. The process is observed continuously on a long time interval [0, T], T→∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T\rightarrow \infty $$\end{document}. We prove that the statistical model is locally asymptotic mixed normal and the maximum likelihood estimator is asymptotically efficient.
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页码:553 / 570
页数:17
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