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Linear dynamic errors-in-variables models

Published online by Cambridge University Press:  14 July 2016

M. Deistler
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Abstract

Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.

Keywords

LINEAR DYNAMIC SYSTEMSIDENTIFIABILITYCAUSALITY

Information

Type
Part 1—Structure and General Methods for Time Series
Information
Journal of Applied Probability , Volume 23 , Issue A: Essays in Time Series and Allied Processes , 1986 , pp. 23 - 39
Copyright
Copyright © 1986 Applied Probability Trust 

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