Perspectives on Errors-In-Variables Estimation for Dynamic Systems

Torsten Söderström (), Umberto Soverini () and Kaushik Mahata ()
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Torsten Söderström: Uppsala University, Department of Systems and Control, Information Technology
Umberto Soverini: University of Bologna, Department of Electronics, Information Science and Systems
Kaushik Mahata: Uppsala University, Department of Systems and Control, Information Technology

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 271-280 from Springer

Abstract: Abstract The paper gives an overview of various methods for identifying dynamic errors-in-variables systems. Several approaches are classified by how the original information in time-series data of the noisy input and output measurements is condensed before further processing. For some methods, such as instrumental variable estimators, the information is condensed into a nonsymmetric covariance matrix as a first step before further processing. In a second class of methods, where a symmetric covariance matrix is used instead, the Frisch scheme and other bias-compensation approaches appear. When dealing with the estimation problem in the frequency domain, a milder data reduction typically takes place by first computing spectral estimators of the noisy input-output data. Finally, it is also possible to apply maximum likelihood and prediction error approaches using the original time-domain data in a direct fashion. This alternative will often require quite high computational complexity but yield good statistical efficiency.

Keywords: dynamic error-in-variables; system identification; instrumental variables; Frisch scheme; prediction error. (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1007/978-94-017-3552-0_24

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