Bank filters for ML parameter estimation via the expectation-maximization algorithm: the continuous-time case

In this paper we consider continuous-time partially observed systems in which the parameters are unknown. We employ conditional moment generating functions of integrals and stochastic integrals to derive new maximum-likelihood (ML) parameter estimates which are required in the implementation of the expectation-maximization algorithm. Each parameter is estimated by a bank of Kalman filters consisting of four statistics: two are the Kalman filter statistics while the remaining two have the structure of the Kalman filter driven by the innovations process