Parameter estimation for general Gaussian processes with discrete observations.

Soukaina Douissi, University Cadi Ayyad Marrakech, Morocco

In this talk we give a general framework to study parameter estimation problems for general Gaussian sequences. We use tools from analysis on Wiener space. No assumption of stationarity is required. The only assumptions made on the sequence are the existence of an asymptotic variance, that a least-squares-type estimator for this variance parameter has a bias and a variance which can be controlled, and that the sequence's covariance function, which may exhibit long memory, has a no-worse memory than that of fractional Brownian motion with Hurst parameter not greater than 3/4. The applications we give concern the estimation of the asymptotic variance for various fractional-noise-driven Ornstein-Uhlenbeck processes. This is joint work with Khalifa Es-Sebaiy (Kuwait University, Kuwait) and Frederi Viens (Michigan State University, USA).

Please direct all questions to the following contacts - Your question will be routed to get you an answer as quickly as possible.

• MSUStatSymposium2018@stt.msu.edu
• Michigan State University
  Department of Statistics and Probability
  Room C413 Wells Hall
  619 Red Cedar Road
  East Lansing, MI 48824