Titlebook: Information Criteria and Statistical Modeling; Sadanori Konishi,Genshiro Kitagawa Book 2008 Springer-Verlag New York 2008 Akaike informati

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Statistical Models,s are used to acquire various types of information in the model-building process. By using regression and time series models as specific examples, we also discuss why evaluation of statistical models is necessary.

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Statistical Modeling by AIC,as comparisons of several statistical models. In this chapter, we consider using the AIC for various statistical inference problems such as checking the equality of distributions, determining the bin size of a histogram, selecting the order for regression models, detecting structural changes, determ

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Generalized Information Criterion (GIC),ized information criterion, GIC [Konishi and Kitagawa (1996)]. The GIC can be applied to evaluate statistical models constructed by various types of estimation procedures including the robust estimation procedure and the maximum penalized likelihood procedure. Section 5.1 describes the fundamentals

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Statistical Modeling by GIC,complex structure. Crucial issues associated with nonlinear modeling are the choice of adjusted parameters including the smoothing parameter, the number of basis functions in splines and .-splines, and the number of hidden units in neural networks. Selection of these parameters in the modeling proce

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Theoretical Development and Asymptotic Properties of the GIC,ivalently, the expected log-likelihood of a statistical model for prediction..In this chapter, we introduce a general framework for constructing information criteria in the context of functional statistics and give technical arguments and a detailed derivation of the generalized information criterio

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Bayesian Information Criteria,ation criterion (BIC) is described. The BIC is also extended such that it can be applied to the evaluation of models estimated by regularization. Section 9.2 presents Akaike’s Bayesian information criterion (ABIC) developed for the evaluation of Bayesian models having prior distributions with hyperp

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Various Model Evaluation Criteria,ch. The AIC-type criteria were constructed as estimators of the Kullback–Leibler information between a statistical model and the true distribution generating the data or equivalently the expected log-likelihood of a statistical model. In contrast, the Bayes approach for selecting a model was to choo

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