In this paper the problem of computing uncertainty regions for models identified through an instrumental variable technique is considered. Recently, it has been pointed out that, in certain operating conditions, the asymptotic theory of system identification (the most widely used method for model quality assessment) may deliver unreliable confidence regions. The aim of this paper is to show that, in an instrumental variable setting, the asymptotic theory exhibits a certain “robustness” that makes it reliable even with a moderate number of data samples. Reasons for this are highlighted in the paper through a theoretical analysis and simulation examples.

The asymptotic model quality assessment for instrumental variable identification revisited

GARATTI, SIMONE;BITTANTI, SERGIO
2006-01-01

Abstract

In this paper the problem of computing uncertainty regions for models identified through an instrumental variable technique is considered. Recently, it has been pointed out that, in certain operating conditions, the asymptotic theory of system identification (the most widely used method for model quality assessment) may deliver unreliable confidence regions. The aim of this paper is to show that, in an instrumental variable setting, the asymptotic theory exhibits a certain “robustness” that makes it reliable even with a moderate number of data samples. Reasons for this are highlighted in the paper through a theoretical analysis and simulation examples.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/553293
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