Information Criteria and Statistical Modeling
Publication year: 2008
ISBN: 978-0-387-71887-3
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One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Subject: Mathematics and Statistics, Akaike information criterion, Bayesian approach, Computer, Estimator, Information, Likelihood, Statistical Models, bioinformatics, computer science, model selection and evaluation, modeling, nonlinear modeling, statistical modeling
