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On the Exponentially Embedded Family (EEF) Rule for Model Order Selection
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2012 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 19, no 9, p. 551-554Article in journal (Refereed) Published
Abstract [en]

Model selection is an important task in many signal processing applications. In this letter, we present a generalized likelihood ratio (GLR)-based derivation of the recently proposed EEF rule in an attempt to cast EEF in the main stream of model order selection approaches and provide further insights into its theoretical foundations. We also show that EEF can be expected to behave asymptotically (in the number of data samples) similarly to the Bayesian information criterion (BIC). To evaluate the finite sample performance we consider two numerical examples, including the selection of the number of components in a Gaussian mixture model (GMM), by means of which we show that EEF behaves similarly to BIC.

Place, publisher, year, edition, pages
2012. Vol. 19, no 9, p. 551-554
Keyword [en]
Bayesian information criterion (BIC), exponentially embedded family (EEF), Gaussian mixture model (GMM), generalized likelihood ratio (GLR), model order selection
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:uu:diva-179551DOI: 10.1109/LSP.2012.2206583ISI: 000306520600001OAI: oai:DiVA.org:uu-179551DiVA: diva2:545463
Available from: 2012-08-20 Created: 2012-08-20 Last updated: 2018-01-12Bibliographically approved

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Stoica, PeterBabu, Prabhu

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