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    An improved method for model selection based on Information Criteria

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    Information 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.Comment: 5 pages, 8 figures, IEEE conference Submissio
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