DocumentCode :
3023553
Title :
Linear system identification from non-stationary cross-sectional data
Author :
Goodrich, R.L. ; Caines, P.E.
Author_Institution :
ABT Associates, Cambridge, Massachusetts
fYear :
1979
fDate :
10-12 Jan. 1979
Firstpage :
261
Lastpage :
267
Abstract :
The identification of time invariant linear stochastic systems from cross-sectional data on non-stationary system behavior is considered. A strong consistency and asymptotic normality result for maximum likelihood and prediction error estimates of the system parameters, system and measurement noise covariances and the initial state covariance is proven. A new identifiability property for the system model is defined and appears in the set of conditions for this result. The non-stationary stochastic realization (i.e., covariance factorization) theorem in [1] describes sufficient conditions for the identifiability property to hold. An application illustrating the use of a computer program implementing the identification method is presented.
Keywords :
Econometrics; Kalman filters; Linear systems; Noise measurement; Parameter estimation; Psychology; Technological innovation; Time invariant systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control including the 17th Symposium on Adaptive Processes, 1978 IEEE Conference on
Conference_Location :
San Diego, CA, USA
Type :
conf
DOI :
10.1109/CDC.1978.267933
Filename :
4046120
Link To Document :
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