Minimum density power divergence estimator for diffusion processes

Sangyeol Lee, Junmo Song

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In this paper, we consider the robust estimation for a certain class of diffusion processes including the Ornstein-Uhlenbeck process based on discrete observations. As a robust estimator, we consider the minimum density power divergence estimator (MDPDE) proposed by Basu et al. (Biometrika 85:549-559, 1998). It is shown that the MDPDE is consistent and asymptotically normal. A simulation study demonstrates the strong robustness of the MDPDE.

Original languageEnglish
Pages (from-to)213-236
Number of pages24
JournalAnnals of the Institute of Statistical Mathematics
Issue number2
StatePublished - Apr 2013


  • Diffusion processes
  • Discretely observed sample
  • Minimum density power divergence estimator
  • Robustness
  • The Ornstein-Uhlenbeck process


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