The relation of the CCA subspace method to a balanced reduction of an autoregressive model.
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Publication:1421323
DOI10.1016/S0304-4076(03)00144-1zbMath1033.62080MaRDI QIDQ1421323
Wolfgang Scherrer, Anders Dahlén
Publication date: 26 January 2004
Published in: Journal of Econometrics (Search for Journal in Brave)
Canonical correlation analysisAsymptotic analysisAutoregressive-modelingMaximum entropy covariance extensionStochastically balanced form
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Identification in stochastic control theory (93E12)
Related Items (6)
Data-driven predictive control in a stochastic setting: a unified framework ⋮ Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data ⋮ ESTIMATING LINEAR DYNAMICAL SYSTEMS USING SUBSPACE METHODS ⋮ The role of vector autoregressive modeling in predictor-based subspace identification ⋮ Asymptotic properties of subspace estimators ⋮ Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs
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- Canonical correlation analysis, approximate covariance extension, and identification of stationary time series
- A covariance extension approach to identification of time series
- A transformation approach to stochastic model reduction
- Lossless chain scattering matrices and optimum linear prediction: The vector case
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