269
Views
7
CrossRef citations to date
0
Altmetric
Original Articles

Global convergence conditions in maximum likelihood estimation

&
Pages 475-490 | Received 20 Oct 2011, Accepted 12 Jan 2012, Published online: 07 Feb 2012
 

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.

Acknowledgements

The authors would like to thank Dr. Daniel Coca and Dr. Tim Breikin for their constructive comments on the way of discussing the simulation examples. The authors are sincerely grateful to the anonymous reviewers for their useful suggestions in the literature survey, theory presentation and simulation illustration.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,709.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.