A covariance extension approach to identification of time series

DOI10.1016/S0005-1098(99)00172-7zbMath0986.93069MaRDI QIDQ1975568

Anders Dahlén, Anders Lindquist, Jorge Mari

Publication date: 13 June 2002

Published in: Automatica (Search for Journal in Brave)

zbMATH Keywords

stochastic model reductionAR-modelmaximum entropy extensioncovariance extensionidentification of time seriespartial covariance sequence

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12) System structure simplification (93B11)

Related Items (6)

Convergence of the least-squares method with a polynomial regularizer for the infinite-dimensional autoregression equation ⋮ Spectral estimation by least-squares optimization based on rational covariance extension ⋮ Strong consistency of the regularized least-squares estimates of infinite autoregressive models ⋮ The relation of the CCA subspace method to a balanced reduction of an autoregressive model. ⋮ Banded Regularization of Autocovariance Matrices in Application to Parameter Estimation and Forecasting of Time Series ⋮ Cepstral identification of autoregressive systems

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A tighter relative-error bound for balanced stochastic truncation
• Linear identification of ARMA processes
• The distribution of zeros of asymptotically extremal polynomials
• Consistent autoregressive spectral estimates
• Experimental evidence showing that stochastic subspace identification methods may fail
• Subspace algorithms for the stochastic identification problem
• On the \(L^{\infty}\) consistency of \(L^ 2\) estimators
• Canonical correlation analysis, approximate covariance extension, and identification of stationary time series
• Estimation and information in stationary time series
• A transformation approach to stochastic model reduction
• EFFICIENT ESTIMATION OF PARAMETERS IN MOVING-AVERAGE MODELS
• The Fitting of Time-Series Models
• All optimal Hankel-norm approximations of linear multivariable systems and theirL,^∞-error bounds†
• Realization Theory for Multivariate Stationary Gaussian Processes
• ARMA order estimation via matrix perturbation theory
• Asymptotic properties of black-box identification of transfer functions
• Realization of power spectra from partial covariance sequences
• Regression and time series model selection in small samples
• Hard frequency-domain model error bounds from least-squares like identification techniques
• Asymptotic properties of the least-squares method for estimating transfer functions and disturbance spectra
• ESTIMATION OF AUTOREGRESSIVE MOVING-AVERAGE MODELS VIA HIGH-ORDER AUTOREGRESSIVE APPROXIMATIONS
• A Convex Optimization Approach to the Rational Covariance Extension Problem
• On the Nonlinear Ddynamics of Fast Filtering Algorithms
• A complete parameterization of all positive rational extensions of a covariance sequence

This page was built for publication: A covariance extension approach to identification of time series