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Abstract: A bias Compensated Total Least Squares estimator is proposed for models which are linear-in-theparameters and non-linear in the perturbed input and output signals. The estimates are obtained using a non-iterative and numerically stable method. Strong consistency of both the Generalized Total Least Squares and the Compensated Total Least Squares estimates are proven. The theoretical properties are verified through simulations. (Update)
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BibTeX entry: (Update)
@misc{ vandersteen-use,
author = "Gerd Vandersteen",
title = "On the Use of Compensated Total Least Squares in System Identification",
url = "citeseer.ist.psu.edu/403215.html" }
Citations (may not include all citations):519 System Identification: Theory for the User (context) - Ljung - 1987
102 The Total Least Squares Problem: Computational Aspects and A.. (context) - Van Huffel, Vandewalle - 1991
101 Adaptive Filtering, Prediction and Control (context) - Goodwin, Sin - 1984
24 An Introduction to Identification (context) - Norton - 1986
18 Computing the Generalized Singular Value Decomposition (context) - Paige - 1986
9 Bias in Non-linear Estimation (context) - Box - 1971
4 Time Series: Data Analysis and Theory (expanded edition (context) - Brillinger - 1981
4 General Framework for Asymptotic Properties of Generalized W.. - Vandersteen, Van hamme et al. - 1996
4 Bias Correction in Least Squares Identification (context) - Stoica, Sderstrm - 1982
3 Kendall's Advanced Theory of Statistics - 5th edition (context) - Stuart, Ord - 1987
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