1 research outputs found
An improved method for model selection based on Information Criteria
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