Continuous‐time Dynamical Systems With Sampled Data, Errors of Measurement and Unobserved Components

Maximum likelihood estimation of sampled continuous‐time stochastic processes is considered. The likelihood is directly maximized with respect to the original structural parameters using a scoring algorithm with exact analytical derivatives. Furthermore, the case of unobserved states and errors of measurement is treated via EM and quasi‐Newton algorithms. The proposed methods are illustrated with simulation studies and analysis of sunspot activity.