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Least squares estimator for a class of subdiffusion processes

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  • Huiyan Zhao
  • Chongqi Zhang
  • Yu Guo
  • Sheng Lin

Abstract

In this article, we consider the parameter estimation problem for a class of subdiffusion processes which are characterized by the time-changed Ornstein–Uhlenbeck processes. Least squares method is used to obtain the estimator for the drift coefficient. First, we get the strong consistency, asymptotical normality and asymptotical mixed normality for the estimator on the condition that we can observe the process continuously. After that, weak consistent and asymptotic properties are derived basing on discrete observations when the time-change process is an inverse α-stable (45

Suggested Citation

  • Huiyan Zhao & Chongqi Zhang & Yu Guo & Sheng Lin, 2022. "Least squares estimator for a class of subdiffusion processes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(15), pages 5342-5363, June.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:15:p:5342-5363
    DOI: 10.1080/03610926.2020.1838546
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