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The bias compensated least squares approach for errors-in-variables model identification is examined in a new framework, where it is allowed to prefilter the observed input-output data prior to the estimation process. A statistical analysis of the estimation algorithm is presented. Subsequently, it is shown how these prefilters and the weighting matrix can be tuned in order to optimize the estimation...
Fitting multidimensional parametric models in frequency domain using nonparametric noise models is considered in this paper. A nonparametric estimate of the noise statistics is obtained from a finite number of independent data sets. The estimated noise model is then substituted for the the true noise covariance matrix in the maximum likelihood loss function to obtain suboptimal parameter estimates...
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