Consistency and relative efficiency of subspace methods

DOI10.1016/0005-1098(95)00089-6zbMath0846.93024OpenAlexW2061220935MaRDI QIDQ1911279

K. Peternell, Wolfgang Scherrer, Manfred Deistler

Publication date: 22 September 1996

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0005-1098(95)00089-6

zbMATH Keywords

identificationconsistencyrelative efficiencysubspace methodleast-squares procedure

Mathematics Subject Classification ID

Related Items (26)

Vector autoregressive moving average identification for macroeconomic modeling: a new methodology ⋮ Subspace-based fault detection robust to changes in the noise covariances ⋮ Criteria for informative experiments with subspace identification ⋮ Dual time-frequency domain system identification ⋮ Consistency of subspace methods for signals with almost-periodic components ⋮ The relation of the CCA subspace method to a balanced reduction of an autoregressive model. ⋮ State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI ⋮ ESTIMATING LINEAR DYNAMICAL SYSTEMS USING SUBSPACE METHODS ⋮ Analysis of the asymptotic properties of the MOESP type of subspace algorithms ⋮ Subspace identification for non-linear systems with measured-input non-linearities ⋮ Subspace identification – a Markov parameter approach ⋮ Bootstrap-based estimates of uncertainty in subspace identification methods ⋮ Least-squares estimation of input/output models for distributed linear systems in the presence of noise ⋮ Business cycle analysis and VARMA models ⋮ Asymptotic properties of subspace estimators ⋮ Subspace-based continuous-time identification of fractional order systems from non-uniformly sampled data ⋮ Estimating ARMAX systems for multivariate time series using the state approach to subspace algorithms ⋮ Fast estimation methods for time-series models in state–space form ⋮ Model specification and selection for multivariate time series ⋮ Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs ⋮ USING SUBSPACE METHODS FOR ESTIMATING ARMA MODELS FOR MULTIVARIATE TIME SERIES WITH CONDITIONALLY HETEROSKEDASTIC INNOVATIONS ⋮ MULTIVARIATE AR SYSTEMS AND MIXED FREQUENCY DATA: G-IDENTIFIABILITY AND ESTIMATION ⋮ Forecasting linear dynamical systems using subspace methods ⋮ Subspace estimation and prediction methods for hidden Markov models ⋮ Consistency analysis of subspace identification methods based on a linear regression approach ⋮ Estimating cointegrated systems using subspace algorithms

Cites Work

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• Identification of echelon canonical forms for vector linear processes using least squares
• Subspace algorithms for the stochastic identification problem
• A new algorithm for recursive estimation of parameters in controlled ARMA processes
• Multivariate linear time series models
• Structure and order estimation of multivariable stochastic processes
• Recursive estimation of mixed autoregressive-moving average order
• Stochastic theory of minimal realization
• Markovian Representation of Stochastic Processes by Canonical Variables

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