Front Matter; Concept of Statistical Modeling; Statistical Models; Information Criterion; Statistical Modeling by AIC; Generalized Information Criterion (GIC); Statistical Modeling by GIC; Theoretical Development and Asymptotic Properties of the GIC; Bootstrap Information Criterion; Bayesian Information Criteria; Various Model Evaluation Criteria; Back Matter
Summary
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. 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 crite
Bibliography
Includes bibliographical references (pages 255-267) and index
