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Asymptotic Normality of Prediction Error Estimators for Approximate System Models
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Harvard University, MA, USA.
1978 (engelsk)Rapport (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University , 1978. , s. 31
Serie
LiTH-ISY-I, ISSN 8765-4321 ; 240
Emneord [en]
Output-error methods, Maximum likelihood, System identification
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-104496OAI: oai:DiVA.org:liu-104496DiVA, id: diva2:697207
Tilgjengelig fra: 2014-02-17 Laget: 2014-02-17 Sist oppdatert: 2014-02-17

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