An improved method for model selection based on Information Criteria

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dc.creatorCoq, Guilhem
dc.creatorAlata, Olivier
dc.creatorArnaudon, Marc
dc.creatorOlivier, Christian
dc.date2007-02-19
dc.date.accessioned2026-07-07T08:08:41Z
dc.date.available2026-07-07T08:08:41Z
dc.descriptionInformation criteria are an appropriate and widely used tool for solving model selection problems. However, different ways to use them exist, each leading to a more or less precise approximation of the sought model. In this paper, we mainly present two methods of utilisation of information criteria : the classical one which is generally used and an alternative one, more precise but requiring a little more calculations. Those methods are compared on 1-D and 2-D autoregressive models ; we use a synthetized process for the 1-D case and texture images for the 2-D case. We also work with the original phi_beta criterion which includes all others usual criteria such as AIC, BIC, and phi.
dc.description5 pages, 8 figures, IEEE conference Submission
dc.identifierhttps://arxiv.org/abs/math/0702540
dc.identifierhttp://arxiv.org/abs/math/0702540
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131349
dc.subjectStatistics Theory
dc.subjectProbability
dc.titleAn improved method for model selection based on Information Criteria
dc.typetext

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