Optimal rates for parameter estimation of stationary Gaussian processes
By Khalifa Es-Sebaiy, Frederi G. Viens. Stochastic Processes and their Applications, 2016
49 citations recorded by the source corpus.
We study rates of convergence in central limit theorems for partial sum of functionals of general stationary and non-stationary Gaussian sequences, using optimal tools from analysis on Wiener space. We apply our result to study drift parameter estimation problems for some stochastic differential equations driven by fractional Brownian motion with fixed-time-step observations.
Subfields: Finance, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/e5c023ddd0f65af9d711625fc05b75d788b0a055
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- Ivan Nourdin
- T. T. Diu Tran
- Giovanni Peccati
- David Nualart
- Frederi G. Viens
- Gesine Reinert
- Khalifa Es-Sebaiy
- Anthony R'eveillac
Citations in the corpus
- Statistical inference for Vasicek-type model driven by Hermite processes (2017, background): “Our paper is relevant to the literature on parameter estimation for processes with Gaussian and non-Gaussian long-memory processes, including [1, 5, 6, 7, 8, 9, 12, 13, 14, 16, 27].”