liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Asymptotic Normality of Prediction Error Estimators for Approximate System Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Harvard University, MA, USA.
1978 (English)Report (Other academic)
Abstract [en]

A general class of parameter estimation methods for stochastic dynamical systems is studied. The class contains the least squares method, output-error methods, the maximum likelihood method and several other techniques. It is shown that the class of estimates so obtained are asymptotically normal and expressions for the resulting asymptotic covariance matrices are given. The regularity conditions that are imposed to obtain these results are fairly weak. It is, for example, not assumed that the true system can be described within the chosen model set, and, as a consequence, the results in this paper form a part of the so-called approximate modeling approach to system identification. It is also noteworthy that arbitrary feedback from observed system outputs to observed system inputs is allowed and that stationarity is not required.

Place, publisher, year, edition, pages
Linköping: Linköping University , 1978. , 31 p.
LiTH-ISY-I, ISSN 8765-4321 ; 240
Keyword [en]
Output-error methods, Maximum likelihood, System identification
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-104496OAI: diva2:697207
Available from: 2014-02-17 Created: 2014-02-17 Last updated: 2014-02-17

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Ljung, Lennart
By organisation
Automatic ControlThe Institute of Technology
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 30 hits
ReferencesLink to record
Permanent link

Direct link