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  • 标题:Statistical inference for generalized Ornstein-Uhlenbeck processes
  • 本地全文:下载
  • 作者:Denis Belomestny ; Vladimir Panov
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2015
  • 卷号:9
  • 期号:2
  • 页码:1974-2006
  • DOI:10.1214/15-EJS1063
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:In this paper, we consider the problem of statistical inference for generalized Ornstein-Uhlenbeck processes of the type \[X_{t}=e^{-\xi_{t}}\left(X_{0}+\int_{0}^{t}e^{\xi_{u-}}du \right), \] where $\xi_{s}$ is a Lévy process. Our primal goal is to estimate the characteristics of the Lévy process $\xi$ from the low-frequency observations of the process $X$. We present a novel approach towards estimating the Lévy triplet of $\xi$, which is based on the Mellin transform technique. It is shown that the resulting estimates attain optimal minimax convergence rates. The suggested algorithms are illustrated by numerical simulations.
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