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.
Asymptotic variance of subspace methods by data orthogonalization andmodel decoupling: a comparative analysis
CHIUSO, ALESSANDRO;
2004
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.
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