@TechReport{ it:2001-011,
author = {Torsten S{\"o}derstr{\"o}m and Umberto Soverini and
Kaushik Mahata},
title = {Perspectives on errors-in-variables estimation for dynamic
systems},
institution = {Department of Information Technology, Uppsala University},
department = {Division of Systems and Control},
year = {2001},
number = {2001-011},
month = may,
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.
The paper is also presenting various properties of
parameter estimators for the errors-in-variables problem,
and a few conjectures are included, as well as some
perspectives and experiences by the authors. }
}