The paper concerns the estimation of a smooth signal S(t) and its derivatives in the presence of a noise depending on a small parameter ε based on a partial observation. A nonlinear Kalman-type filter is proposed to perform on-line estimation. For the signal S in a given class of smooth functions, the convergence rate for the estimation risks, as ε → 0, is obtained. It is proved that such rates are optimal in a minimax sense. In contrast to the complete observation case, the rates are reduced, due to incomplete information.
Problems of Information Transmission – Springer Journals
Published: Jan 24, 2006
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