@TechReport{	  it:2005-019,
  author	= {Torsten S{\"o}derstr{\"o}m},
  title		= {Computing the Covariance Matrix for {PEM} Estimates and
		  the {C}ramer-{R}ao Lower Bound for Linear State Space
		  Models},
  institution	= {Department of Information Technology, Uppsala University},
  department	= {Division of Systems and Control},
  year		= {2005},
  number	= {2005-019},
  month		= jun,
  abstract	= {The paper presents a complete and comprehensive algorithm
		  for computing the asymptotic accuracy of estimated state
		  space models. The parameterization is assumed to be give a
		  uniquely identifiable system, but is otherwise general. It
		  is assumed that the system matrices and the noise
		  characteristics are smooth functions of the unknown
		  parameters. Expressions for the asymptotic covariance
		  matrix of the parameter estimates are derived for some
		  variants of the prediction error method. As a special case
		  for Gaussian distributed data, the Cram{\'e}r-Rao bound and
		  the covariance matrix for maximum likelihood estimates are
		  obtained.}
}