Estimation for Partially Observed Mean-reversion Type Stochastic Systems.

This paper is concerned with the parameter estimation problem for partially observed mean-reversion type stochastic systems. The Girsanov transformation is used to simplify the equation because of the expression of the drift coefficient. The suboptimal estimation of the state is obtained by constructing the extended Kalman filtering equation. The likelihood function is provided based on state estimation equation. The strong consistency of the estimator is proved by applying maximal inequality for martingales, Borel-Cantelli lemma and uniform ergodic theorem. An example is given to verify the effectiveness of the estimation methods. [ABSTRACT FROM AUTHOR]