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Journal:International journal of control

Key Words:MLE; optimisation; global/local convergence; consistency; asymptotic efficiency and sufficiency; non-local-minimum conditions

Abstract:Maximum likelihood estimation has been widely applied in system identification because of consistency, its
asymptotic efficiency and sufficiency. However, gradient-based optimisation of the likelihood function might end
up in local convergence. In this article we derive various new non-local-minimum conditions in both open and
closed-loop system when the noise distribution is a Gaussian process. Here we consider different model
structures, in particular ARARMAX, BJ and OE models.

Co-author:William Heath

First Author:Yiqun Zou*

Indexed by:Journal paper

Volume:85

Issue:5

Page Number:475–490

Translation or Not:no

Date of Publication:2012-05-05

Included Journals:SCI

Links to published journals:https://www.tandfonline.com/doi/abs/10.1080/00207179.2012.658085

Date of Publication:2012-05-05