Estimating Functions for Discretely
نویسندگان
چکیده
hagen] are generalized to facilitate parameter estimation in discretely observed stochastic diierential equations, where the observations are corrupted by additive white noise. This generalization provides an optimal solution to the parameter estimation problem in terms of estimating functions as an alternative to methods based on the Kalman lter or higher order lters. Using Monte Carlo simulation the new method is compared to simple and explicit estimating functions, and a second order nonlinear lter. The study shows that the new method outperforms the other methods and that optimal weights are required for estimating the diiusion parameter.
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