Model quality evaluation in set membership identification
Automatica (Journal of IFAC)
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Brief Finite sample properties of system identification of ARX models under mixing conditions
Automatica (Journal of IFAC)
Brief Non-asymptotic confidence ellipsoids for the least-squares estimate
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Rademacher penalties and structural risk minimization
IEEE Transactions on Information Theory
Rademacher averages and phase transitions in Glivenko-Cantelli classes
IEEE Transactions on Information Theory
Non-asymptotic confidence regions for model parameters in the presence of unmodelled dynamics
Automatica (Journal of IFAC)
Guaranteed non-asymptotic confidence regions in system identification
Automatica (Journal of IFAC)
Least-squares estimation of a class of frequency functions: A finite sample variance expression
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In any real-life identification problem, only a finite number of data points is available. On the other hand, almost all results in stochastic identification pertain to asymptotic properties, that is they tell us what happens when the number of data points tends to infinity. In this paper we consider the problem of assessing the quality of the estimates identified from a finite number of data points. We focus on least squares identification of generalised FIR models and develop a method to produce a bound on the uncertainty in the parameter estimate. The method is data driven and based on tests involving permuted data sets. Moreover, it does not require that the true system is in the model class.