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Multi-model approach to model 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: Digital Signal Processing, Vol. 14, no 5, 399-412 p.Article in journal (Refereed) Published
Abstract [en]

The single-model approach to model selection based on

information criteria, such as AIC or BIC, is omnipresent in the

signal processing literature.

However, any single-model approach picks up only

one model and hence misses the potentially significant information

associated with the other models fitted to the data. In our opinion

this is a drawback:

indeed, depending on the application, even the true model structure

(assuming that there was one) may not be the best choice for the intended

use of the model. The multi-model approach does not suffer

from such a problem:

using nothing more than the values of AIC or BIC it estimates

the a posteriori probabilities of each model under consideration

and then it goes on to use all fitted models in a weighted manner

according to their posterior likelihoods. We show via a numerical

study that the multi-model approach can outperform the single-model

approach in terms of statistical accuracy, without unduly increasing

the computational burden. The first goal of this paper is to advocate

the multi-model approach. A second goal is to introduce some guidelines

for numerically studying the performance of a model selection rule.

Place, publisher, year, edition, pages
2004. Vol. 14, no 5, 399-412 p.
Keyword [en]
model selection, AIC, BIC, model averaging
Identifiers
URN: urn:nbn:se:uu:diva-70182OAI: oai:DiVA.org:uu-70182DiVA: diva2:98093
Available from: 2005-05-04 Created: 2005-05-04 Last updated: 2011-01-12

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Stoica, PeterSelén, Yngve

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