Comparing some classes of bias-compensating least squares methods

Authors:
Torsten SöDerströM
Affiliations:
-
Venue:
Automatica (Journal of IFAC)
Year:
2013

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Abstract

Three different classes of bias-compensating least squares identification methods are compared, and shown to be identical. It is also discussed how the user parameters in the classes can be chosen to achieve optimal accuracy of the parameter estimates.