Maximum likelihood estimation with reduced sensitivity functions

A new approach is proposed for maximum likelihood estimation in linear constant coefficient dynamic systems with general structure. The formulation is developed in terms of the minimum number of variables required to specify the parameter sensitivity of state variables, called the reduced sensitivity functions. An efficient computation procedure is obtained for parameter estimation from time domain or frequency domain data. The importance of end conditions in the frequency domain approach is analyzed.