@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. }
}