Consistency of least squares estimation to the parameter for stochastic differential equations under distribution uncertainty
Abstract
Under distribution uncertainty, on the basis of discrete data we investigate the consistency of the least squares estimator (LSE) of the parameter for the stochastic differential equation (SDE) where the noise are characterized by $G$Brownian motion. In order to obtain our main result of consistency of parameter estimation, we provide some lemmas by the theory of stochastic calculus of sublinear expectation. The result shows that under some regularity conditions, the least squares estimator is strong consistent uniformly on the prior set. An illustrative example is discussed.
 Publication:

arXiv eprints
 Pub Date:
 April 2019
 arXiv:
 arXiv:1904.12701
 Bibcode:
 2019arXiv190412701F
 Keywords:

 Mathematics  Statistics Theory;
 60H10
 EPrint:
 17 pages, 2 tables