On inference for fractional differential equations

Research output: Contribution to journalArticlepeer-review


Based on Malliavin calculus tools and approximation results, we show how to compute a maximum likelihood type estimator for a rather general differential equation driven by a fractional Brownian motion with Hurst parameter H>1/2. Rates of convergence for the approximation task are provided, and numerical experiments show that our procedure leads to good results in terms of estimation.

Original languageEnglish (US)
Pages (from-to)29-61
Number of pages33
JournalStatistical Inference for Stochastic Processes
Issue number1
StatePublished - Apr 2013
Externally publishedYes


  • Fractional brownian motion
  • Inference for stochastic processes
  • Malliavin calculus
  • Stochastic differential equations

ASJC Scopus subject areas

  • Statistics and Probability


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