Approximate Conditional Least Squares Estimation of a Nonlinear State-Space Model via Unscented Kalman Filter
نویسندگان
چکیده
We consider the problem of estimating a nonlinear state-space model whose state process is driven by an ordinary differential equation (ODE) or a stochastic differential equation (SDE), with discrete-time data. We propose a new estimation method by minimizing the conditional least squares (CLS) with the conditional mean function computed approximately via unscented Kalman filter (UKF). We derive conditions
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