Information criteria and statistical modeling pdf
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Model Selection With AIC
The Akaike information criterion AIC derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach.
Akaike information criterion
The problem of evaluating the goodness of statistical models is fundamental and of importance in various fields of statistics, natural sciences, neural networks, engineering, economics, etc. AIC is a criterion for evaluating the models estimated by the maximum likelihood method. With the development of various non-linear modeling techniques, the construction of criteria which enable us to evaluate various types of statistical models has been required. The aim of this paper is to give a systematic account of some recent developments in model evaluation criteria from information-theoretic and Bayesian points of views. We intend to provide a basic expository account of the fundamental principles behind information criteria. We also discuss the application of the bootstrap methods in model evaluation problems. Already have an account?