The statistical theory of linear systems
The statistical theory of linear systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Consistency and relative efficiency of subspace methods
Automatica (Journal of IFAC) - Special issue on trends in system identification
International Journal of Computer Vision
An improved bias-compensation approach for errors-in-variables model identification
Automatica (Journal of IFAC)
Journal of Multivariate Analysis
Computers and Electronics in Agriculture
Some facts about the choice of the weighting matrices in Larimore type of subspace algorithms
Automatica (Journal of IFAC)
Analysis of the asymptotic properties of the MOESP type of subspace algorithms
Automatica (Journal of IFAC)
Bootstrap-based estimates of uncertainty in subspace identification methods
Automatica (Journal of IFAC)
Asymptotic properties of subspace estimators
Automatica (Journal of IFAC)
Subspace-based fault detection robust to changes in the noise covariances
Automatica (Journal of IFAC)
Hi-index | 22.16 |
Asymptotic normality for a class of subspace algorithms, which estimate the state in a first step, is derived. Expressions for the asymptotic variance are given. Linear systems with unobserved white noise inputs are considered. A class of subspace estimates for the system matrices obtained by estimating the state in the first step is analyzed. The main result presented here states asymptotic normality of subspace estimates. In addition, a consistency result for the system matrix estimates is given. An algorithm to compute the asymptotic variances of the estimates is presented. In a final section the implications of the result are discussed.