Asymptotic variance of subspace methods by data orthogonalization and model decoupling: a comparative analysis

Authors:
Alessandro Chiuso;Giorgio Picci
Affiliations:
Department of Information Engineering, University of Padova, via Gradenigo 6/a, 35131 Padova, Italy;Department of Information Engineering, University of Padova, via Gradenigo 6/a, 35131 Padova, Italy
Venue:
Automatica (Journal of IFAC)
Year:
2004

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Abstract

This is a companion of the paper Chiuso and Picci (2004d) where we do asymptotic error analysis of a weighted PI-MOESP type method and compare accuracy with respect to estimates obtained by customary ''joint'' subspace methods. The analysis shows that, under certain conditions, methods based on orthogonal decomposition of the input-output data and block-decoupled parametrization perform better than traditional joint-model based methods in the circumstance of nearly parallel regressors.