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On information criteria and the generalized likelihood ratio test of model order selection
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systems and Control.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systems and Control.
2004 (English)In: IEEE Signal Processing Letters, Vol. 11, no 10, 794-797 p.Article in journal (Refereed) Published
Abstract [en]

The Information Criterion (IC) rule and the Generalized Likelihood

Ratio Test (GLRT) have been usually considered to be two rather different

approaches to model order selection. However, we show here that a natural

implementation of the GLRT is in fact equivalent to the IC rule. A consequence

of this equivalence is that a specific IC rule, such as AIC or BIC, can be

viewed as a more direct way of implementing a GLRT with a specific

threshold. Another consequence of the equivalence, which is emphasized

herein, is a possibly original way of exploiting the information provided

by the local behavior of an IC for selecting the structure of

sparse models (the parameter vectors of which comprise ``many''

elements equal to zero).

Place, publisher, year, edition, pages
2004. Vol. 11, no 10, 794-797 p.
Identifiers
URN: urn:nbn:se:uu:diva-70176OAI: oai:DiVA.org:uu-70176DiVA: diva2:98087
Available from: 2005-05-06 Created: 2005-05-06 Last updated: 2011-01-12

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Stoica, PeterSelén, Yngve

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