Title :
Prediction error identification methods for stationary stochastic processes
Author_Institution :
University of Toronto, Toronto, Ontario, Canada
fDate :
8/1/1976 12:00:00 AM
Abstract :
The strong consistency of a general class of prediction error identification methods for stationary stochastic processes is demonstrated. In particular, the strong consistency of the maximum likelihood method for stationary Gaussian processes [4], [5] and of the quadratic loss prediction error method for stationary stochastic processes [1]-[3] follow as special cases of the general result.
Keywords :
Parameter estimation; Prediction methods; Stochastic processes; maximum-likelihood (ML) estimation; Filters; Gaussian processes; Least squares methods; Linear systems; Maximum likelihood estimation; Minimization methods; Parameter estimation; Stochastic processes; Stochastic systems; Yield estimation;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1976.1101304
